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A structured approach to Asset Management within the electricity industry
Utilities Policy 7 (1998) 221–232
A structured approach to Asset Management within the electricity industry R.P. Hoskins
a,c,*
, A.T. Brint b, G. Strbac
c
a EA Technology, Capenhurst, Chester, CH1 6ES, UK CORAS, Department of Computer and Mathematical Sciences, University of Salford, Salford M5 4WT, UK MCEE, Department of Electrical Engineering and Electronics, P.O. Box 88, UMIST, Manchester M60 1QD, UK b
c
Received 8 April 1998; received in revised form 20 October 1998; accepted 10 November 1998
Abstract Pressures are increasing upon utilities world-wide to provide a high quality of service at minimum cost, which requires a well founded Asset Management Policy to ensure network assets remain in a satisfactory condition. This issue is particularly acute in the UK Electricity Supply Industry where assets have attained high levels of reliability and failure data are limited, making Asset Management decisions difficult. By drawing upon examples observed in other industries a structured approach to Asset Management is developed. The approach contains six stages which are explained in detail together with a discussion of practical implementation. It is intended that the framework is sufficiently general that it can be applied to any industrial context, although the particular application to Oil-filled Circuit Breakers is detailed. 1999 Elsevier Science Ltd. All rights reserved. Keywords: Asset Management; Condition information; Maintenance modelling
1. Introduction 1.1. Outline of the problem Many utilities world-wide are presently operating within tightening regulatory environments, and are being forced to address the issues of providing a high quality of service to the customer at a cost deemed adequate by the regulatory body. This necessitates a sensible Asset Management (AM) policy, to ensure that the infrastructure remains in a satisfactory condition. In the past the emphasis was on expanding networks to securely connect whole populations, such as the electricity network or the water, road, gas networks, etc. This has been completed and has resulted in a shift in emphasis from the physical expansion of networks, to a focus on the quality of the service being delivered. Action is thus needed to prevent and counteract severe asset deterioration. However, in the increasingly competitive environment * Corresponding author, c/o Dr G. Strbac MCEE, Department of Electrical Engineering and Electronics, P.O. Box 88, UMIST, Manchester M60 1QD, UK. Tel.: ⫹ 44-0161-200-4803; e-mail: c/o [email protected]
in which the owners of such networks are operating, be they private or public companies, the budgets allocated towards the upkeep of the network are coming under increasing pressure, in an effort to operate at minimum cost. This budget accounts for the inspection, maintenance, refurbishment, replacement, etc. of network items, and is often termed Asset Management. Considerable attention is now being paid to the formulation of AM policy. It is important that the desire to minimise running costs is not at the expense of the investment needed to retain a satisfactory network condition. Failure to invest sufficiently in the maintenance and replacement of such networks could have safety implications, as this may result in items becoming exposed to a larger risk of failure. Such a failure could result in serious injury or even fatality in some cases. Furthermore, it may be the case that the impact of a deferral or reduction in investment is not immediately noticed, but that such a decision leads to considerable increases in the necessary expenditure in future years. This might be brought about by the enforced replacement of items at an earlier time than if they had been maintained more frequently. This possibility that the implications of a reduction in costs may not manifest themselves for a number of years,
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makes decisions concerning AM policy particularly difficult. The range of questions that utility owners have identified and are trying to produce answers to include the following. 1. What is the impact upon risk and cost in extending the interval of a time based maintenance policy? For instance from a 10 year maintenance interval to a 12 year one? 2. At what condition should an item be maintained to ensure that it retains an adequate level of safety? 3. How often should a given device be inspected? Furthermore, what information should be recorded when carrying out such an inspection, and how should the information be exploited for business purposes? 4. When are networks going to require replacement and how should large scale replacement be implemented? 5. What is the variation in cost between the different years of a given Asset Management policy? Furthermore, would a slightly more expensive policy be considered if it ensured that the budget remained more constant from year to year? There is thus a strong need for a thorough approach to making AM policy decisions and determining the associated budget. This paper proposes a comprehensive structure in which suitable quantitative information is combined with a physical model to allow these issues to be addressed. The result is a mathematical model of degradation which can be established in both situations where substantial failure data exist, and those where there is a sparsity of failure data. The advantage of such a degradation model is that it allows predictions to be made as to the future condition of the network and of the impact of different AM actions. This is particularly useful when failure data are lacking and failure rate models are difficult to establish, since the acceptability of different future network conditions can then be used to influence the choice of AM policy. Discussion is given of the use of Markov models to model degradation and these have been applied successfully in transportation and other industries to provide a more thorough quantification of AM decisions. It is intended that the proposed structure is sufficiently general that it can be applied to any particular industrial context, to provide solutions to the above questions. The following subsection describes the specific aims of AM within the UK ESI, with emphasis on the particular difficulties faced and the differences from other industries. This is followed by a discussion of the AM practices observed in various utilities and a critical assessment of what is needed in general. The third section of this paper proposes the general framework for making AM decisions, and explains in detail the specific nature of the different stages of this framework. Follow-
ing this, a discussion is given of the practical implementation of such a framework, and finally the conclusions to be drawn are considered. 1.2. United Kingdom Electricity Supply Industry The motivations behind this paper are the particular AM concerns of the distribution network of the UK Electricity Supply Industry (ESI). This is owned by the Electricity Companies, who are responsible for the operation and upkeep of the network. This consists primarily of four main types of asset: Overhead Lines, Underground Cables, Transformers and Switchgear. The bulk of the network was constructed in the late 1950s and early 1960s and concern is growing as to how the assets should be managed. The main reasons for this increased concern are as follows. 쐌 Privatisation and restructuring of the ESI. This occurred in the UK in 1990 and has since led to increased pressure on budgets. It was observed in Duncan and O’Neill (1995) that “In the battle of the budget Preventive Maintenance is a tempting place to cut since the effects may not be felt for years”. Furthermore, it is necessary for the proposed capital investment of the electricity companies to be accepted by the regulator, who then allows the costs to be passed on to the customer. The external regulator will need convincing that the size of such budgets are determined by a consistent and thorough understanding of the state of the network assets. 쐌 Increasing age of the network. Although the majority of the network is approaching an age of 40 years, some parts of the electricity network are presently over 100 years old. Hence, there is a real need to ensure that the network remains in a satisfactory condition. 쐌 Quality of supply. During past decades when the network was expanding, the principal concern of the ESI was to ensure that all customers were connected, and that the supply of electricity met the consumers’ demand. Nowadays this is rarely an issue and attention has shifted to address questions of quality rather than quantity. Such quality issues include customer interruptions, restoration times and power quality (avoidance of voltage sags, harmonics, etc.) Furthermore, the running costs of the network have also come under the spotlight. To ensure that these factors attain satisfactory targets as set by the regulator, it will be necessary to have a well managed network in operation. 쐌 Catastrophic failures. Though extremely rare these incidents can lead to fatality and serious injury, and hence policy needs to ensure that such failures are avoided. It is essential within the ESI that AM decisions take into
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account the major factors present within the industry. These are: 쐌 High reliability. Most equipment has been installed on the network for a considerable time and has been subject to a preventive maintenance policy. This has resulted in very few failures occurring. The lack of failure data causes difficulty in making inferences about the expected life of an asset and in determining AM policy. 쐌 Safety critical issues. When failures do occur, however, there is the potential for catastrophe. This is particularly true of older switchgear which is filled with oil. Attention will be paid to the formulation of AM policies for Oil-filled Circuit Breakers (OCBs), which are difficult items to deal with, because the above two factors are particularly relevant. Since their installation, these items have failed very infrequently, but some of these failures have led to fatalities. Coupled with the fact that thousands of these items are in operation, many of which are currently approaching the end of their often touted 40 year book or design life, the AM of such items is a major concern of the UK ESI at present.
2. Overview of approaches to AM 2.1. AM within the UK ESI Network items have traditionally been maintained on a fixed time interval basis, that is, the time between successive maintenance operations is constant and usually does not vary between items of the same type. The length of this interval, as Douglas and Booth (1992) observe, is determined by an ad hoc approach combining manufacturer’s recommendations, the experience gained from operating the item, and engineering judgement. Such an interval has tended to be around 10 years, although it does appear that maintenance intervals have been extended in the recent past. In conjunction, a book life is often used to determine the age at which items should be replaced, and this is often around 35–40 years. For some items of equipment in the industry, particularly oil filled switchgear, the purpose of performing maintenance has been primarily to prevent catastrophes from occurring. Thus, although a deferral of maintenance might eventually necessitate an earlier replacement of the item, the real driver is that of safety and to avoid the item being exposed to an increased risk. 2.2. AM within other industries In the present climate, business drivers to reduce costs exist even in publicly owned companies, and the problems outlined in Section 1.1 are faced in most industries
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to a greater or lesser extent. However, the factors of high reliability and safety are not always present within other industries, where different factors may exist. For instance, in the manufacturing industry, failures are usually much more common and do not pose as great a problem to safety. Furthermore, such an environment is often considerably more constrained than the electricity distribution (and transmission) network, due to items being located indoors and over a significantly smaller area. Hence, there is often much more knowledge available about the condition of such items. This results in a much better understanding of the behaviour of such items, and allows the performance of the item throughout its life-cycle to be determined more accurately. A more thorough and detailed formulation of AM policy can then be carried out. Situations similar to that of the UK ESI have been observed in the management of US bridges and roads (often termed pavements in the US). These parts of stateowned infrastructure must address whether postponement of maintenance will lead to a substantial increase in the necessary repair bill in future years. In these industries such problems are often targeted by a Bridge Management System (BMS) or a Pavement Management System (PMS). These are medium to long term plans which describe the expected needs of the infrastructure and look at policy optimisation to ensure that such needs are met in a cost effective manner. It is explained in Scherer and Glagola (1994) how the need for a systematic approach to maintenance and rehabilitation strategies within the transportation system was not identified in the US until the late 1960s. This was mainly provoked by a catastrophic incident involving the collapse of a bridge in 1967. The subsequent Highway Safety Act of 1968 required state highway officials to inspect and rate bridges within their jurisdiction. This led many states to formulate a computational approach to maintenance in the form of a BMS. A BMS is defined in Scherer and Glagola (1994) to be “A rational and systematic approach to organising and carrying out all the activities related to managing a network of bridges. It includes optimising the selection of maintenance and improvement actions to maximise the benefits while minimising the costs.” Since 1993, each US state has been required to have implemented a PMS which was described in Juang and Amirkhanian (1992) as a system involving the “collection and manipulation of data in order to generate information on which rational decisions can be based”. Furthermore, it was described in Golabi et al. (1982) that in the past, roads had been allowed to deteriorate too severely before action was taken, but that when repairs were carried out too much remedial work was performed. Introduction of the PMS and BMS has led to a more thorough understanding of the condition of the net-
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work assets and allowed an examination of different maintenance policies and their associated costs. Furthermore, the US natural gas and electric utilities also operate in a regulatory environment and are required by legislation to construct long term plans detailing network management. Such legislation is outlined in the 1992 Energy Policy Act and is explained in Harshbarger and Greenberg (1994). This states the requirement of each publicly regulated electric utility to “provide adequate and reliable service to its electric customers at the lowest system cost”. The publicly regulated natural gas utilities are required to “minimise life-cycle costs of adequate and reliable utility services to gas consumers”. These requirements are often achieved using the strategies of Least Cost Planning or Integrated Resource Planning as outlined in Harshbarger and Greenberg (1994). This is similar in the water authorities of England and Wales who must submit what is titled an Asset Management Plan to the regulatory body, the Office of Water Services (OFWAT), at regular intervals. This forecasts the condition of the assets the company owns and the costs of necessary maintenance and renewal over a 20 year period, amongst other information. This is explained in O’Hagan (1997). Thus in many utilities, a clear goal has been identified to produce thorough answers to the questions identified in Section 1.1. 2.3. Critical assessment of existing AM policy The traditional time based maintenance policy coupled with a fixed book life approach that has been widely used within the ESI appears to have several shortcomings. 1. It is not clear that such policies are optimal. Although time based policies have been implemented within the industry for a large number of years, there does not appear to be any firm justification as to why they are the optimal type of maintenance policy. A discussion of other possible policy types is given in Section 4.2. 2. It is widely felt nowadays that such policies may be too conservative and that many individual items could have maintenance considerably delayed, or even abolished. However, because of the risks involved a firm justification is needed before this can be carried out. 3. The traditional approach does not provide clear predictions as to the future condition of the network items, or as to when their replacement will be required. It is particularly difficult to firmly justify AM policy for OCBs because of the lack of available data, and the need to avoid catastrophic failure. The solutions that have been observed in other industries, such as the PMS
and BMS, have relied heavily on the existence of a large database of information about the condition of network assets. Furthermore, in other industries the safety factor is often not as critical. For OCBs it is believed that the safety implications dominate the choice of AM policy, and this is emphasised in the HSE recommendations (HSE, 1995). For other items of electrical equipment, and in other industries where the possibility of catastrophe is not as high, maintenance is primarily performed to avoid future increases in cost. Thus, the five general questions posed within Section 1.1 will have differing degrees of importance, depending upon the particular industrial context being considered. However, such questions cannot always be directly addressed using existing methods.
2.4. Analysis of situation
The development of a general framework to provide answers to these questions is the basis of this paper. At the heart of this framework is the need for quantification and modelling of the condition of an item and an understanding of the costs and benefits of available AM actions. Although condition information may not be freely available, it can be obtained with some expenditure, and in the absence of failure data this is imperative. Obtaining suitable condition information may require inspections of plant at various intervals, and the recording of relevant measurements. The frequency and scope of such inspections will need to be determined by balancing the costs of carrying them out with the amount of information required to accurately model AM policy. Within the electricity industry, very large items of transmission and generation plant often have condition monitoring performed on-line in order to provide information as to the state of the item, but this is not financially viable at distribution levels. The National Fault and Interruption Reporting Scheme (NAFIRS) was set up in 1965 with the aim of monitoring the performance of electrical items. However, this only records faults and was not intended to be used as a basis for influencing AM policy. Further, it has been revised on a number of occasions and thus it is difficult to use the NAFIRS database to make informed AM decisions. In summary, it is strongly believed that in the absence of data concerning the failure of items, decisions of Asset Management must take into account other sources of information about the state of the network. In particular, data representing the condition of specific items on the network should allow an understanding to be gained of how items perform and deteriorate over time.
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3. Proposed AM framework
Fig. 1.
Traditional failure rate model.
3.1. Modelling of maintenance policies A considerable number of theoretical mathematical models which describe the effect of maintenance upon an item exist, and comprehensive overviews of these can be found in Pierskalla and Voelker (1976), Sherif and Smith (1981), Valdez-Flores and Feldman (1989), or Cho and Parlar (1991). One of the earliest such models was proposed in Barlow and Hunter (1960) where it is assumed that a maintenance action restores an item to its initial condition. By then introducing the costs of maintenance and replacement, together with the failure rate of an item, it is shown how the optimal time interval between maintenance operations can be found. Many other models extend this to look at incorporating into the modelling process, for instance, the use of inspections, minimisation of downtime, the availability of spare parts, amongst many others. However, models which describe maintenance usually rely heavily on the availability of the failure rate function of the item over time, such as the bathtub curve model described in Billinton and Allan (1996). Such a function is used to determine the best maintenance and replacement actions by optimising expected costs over the planning time horizon. When this approach is feasible, the life of an item will usually be represented by a probability distribution. This distribution will often be constructed from records of previous failures, and techniques for doing so are outlined in Nelson (1992) and in Ansell and Phillips (1994). In effect, failure rate models attempt to uncover a direct link between the age of an item and its probability of failure, which can be regarded as the risk to which the item is exposed at a particular time. This approach is illustrated in Fig. 1. However, in the UK ESI and other utilities, the lack of failure data rules out such an approach. This causes difficulties as emphasised by Pintleton and Gelders (1992), who note that “most maintenance optimisation models presume availability of data. In many cases suitable and correct data are not available.” Thus, an alternative approach is needed.
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3.2. Overview of proposed AM structure The types of failure that an item may suffer can range from minor breakdown requiring only a short time to correct to a full blown explosion of an asset which would necessitate an in-depth investigation and replacement of the whole asset. Thus, it is believed important to be aware of the types of failure that Asset Management policy aims to prevent. Furthermore, in order that such types of failure can be targeted it is thought necessary to possess an understanding of the physical process by which such failures are brought about. This should be determined from expertise and should aim to identify the item components whose deterioration can lead to the targeted types of failure. The failure distribution of the item may be very difficult to determine, and so it is proposed to construct a measure of item condition, as an alternative to the failure probability. To ascertain the condition of an item often requires an inspection, but in the interval between inspections the exact condition will not be known. To overcome this difficulty, a model is proposed to provide estimates of condition during this interval. The model will base the condition of the item upon such factors as its age, usage, location, condition at the last inspection, and so on. In cases where failure data are available, it may then be possible to estimate a measure of risk or failure rate which is related to the condition of the item. The concepts of measures of condition and risk are illustrated in Fig. 2. Where failure data are lacking, such as for a number of items of equipment within the UK ESI, it is unlikely that such a risk measure can be established, other than through elicitation of expert engineering judgement and possibly laboratory tests. In practice it may be the case that as an alternative to a robust risk function, a bound upon the condition measure is used, to characterise the threshold above which the risk is deemed to be unacceptable. Condition information is available and once obtained can be used to determine an appropriate condition function. Once a robust condition function has been determined, predictions can be made of the future behaviour of the item (or technically of its condition (measure)). Examin-
Fig. 2.
Proposed failure rate model.
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ation of the tasks involved in different AM actions, and any available data, may then allow the effect of such actions upon item and component condition to be estimated. This enables predictions of the future condition of an item when different AM policies are implemented. Furthermore, introduction of the costs of the different maintenance actions allows comparisons of different AM policies. A decision can then be taken as to the most suitable AM policy in terms of costs and future network condition. The structure of this proposed methodology is summarised in Fig. 3. The principal drivers of this approach are data relating to the condition of items and components. It is these condition data that are used to supplement the lack of failure data. This approach is characterised by six distinct stages which are discussed together with the application to the particular problem of OCBs. 3.3. Identification of most important failure modes (Step 1) It is common for items to be able to fail in a number of different ways each having a different set of consequences. These are often termed failure modes, and it is essential to identify the failure modes of the greatest consequence that need to be avoided. Asset Management policy should then be targeted at preventing these failure modes.
Fig. 3.
Illustration of overview of proposed method.
For OCBs, as previously stated, the principal aim of AM is to avoid catastrophic failure. 3.4. Identification of components and their relative importance (Step 2) In order to prevent failures occurring it is of utmost importance to understand the mechanism by which failures are brought about. Techniques such as Failure Modes and Effects Analysis (FMEA) and Fault Tree Analysis (FTA), outlined in Andrews and Moss (1993), provide such a method. In particular, FTA aims to describe the failure of an item through the identification of the combinations of component failures, that result in a particular failure mode of the item being realised. The item is viewed as a collection of different components which are physically combined in a particular manner. By linking the component failures through a series of AND and OR gates, the individual component failure probabilities are combined to determine the ‘top event’ probability (i.e. item failure). In the case of OCBs, there is a lack of failure data, and hence the component failure probabilities are very small and are difficult to estimate. Therefore, the components which may contribute to the catastrophic failure of an OCB have been identified, and rather than attempting an extremely detailed modelling involving small probabilities, component condition measures are used as an alternative. The schematic representation of such failures is shown in Fig. 4 and is intended to provide a simplistic view of how catastrophe may be brought about. For example, it might be the case that the arc formed when the OCB opens is not extinguished in the required time, possibly because of the operating mechanism not performing correctly. This could be due to the contacts being drawn apart too slowly (possibly as a result of a weakened spring mechanism), the contacts only opening partially and then getting stuck, or as a result of the contacts being corroded or containing deposits such as carbon. Backup protection should then operate but it is possible that the persistence of the arc will ignite the oil and lead to an explosion. From the investigation, the principal components whose deterioration could contribute towards a catastrophic failure have been identified as the Oil, the External Condition, and the Operating Mechanism. As a result, it is recommended that efforts should be made to obtain information detailing the condition of these three components. By way of example, Bucci et al. (1994) describes how underground cables in New York have been analysed by looking at the individual reliabilities of their constituent components—cable sections, cable joints and network transformers. A second example from Marshall (1997) describes how in measuring the condition of items on the electricity network, Overhead Lines were divided
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Fig. 4.
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Failure process of an OCB.
into the component groups Insulator Fitting, Tower Steel or Conductor. 3.5. Construction of suitable component measures (Step 3) This stage prescribes that condition measures should now be established for those components identified in Step 2. If failure data are available, the failure rate of each component identified in Step 2 can be used to provide a measure of the condition of each component. However, because such failure data are often not available, an alternative measure of condition may be required, together with relevant condition data. An assessment is needed of what constitutes a good measure of a component’s condition, and great importance must be placed on ensuring that the data used to describe this component measure, are of good quality. For instance, consider the choice of condition measure for the oil within an OCB. Several different measures have been observed, for example, the dielectric strength (measured in kV/mm), dissolved moisture content (measured in ppm), or the oil colour (on a scale of 1– 10). These component conditions will only be known when inspection or maintenance has been carried out. During intermediary periods a model is used to provide estimates of condition, as detailed in Step 5 in Section 3.7. However, at present such condition information is not widely available. It is believed that UK electricity companies have begun to see the value in collecting these data, and are starting to implement condition databases. Thus, it is anticipated that such information will become available in the future. A very common example outside of the ESI is that of motor vehicles. It is common when these are traded to quote the age and the mileage of the item, which enables an initial estimate of the vehicle condition to be made. However, more specific information such as the state of the engine or the amount of rust necessitates a detailed inspection. Furthermore, situations have been observed where instead of using a directly measurable quantity to represent condition, a measure is constructed based on a
more human perception. An example taken from Bridge Management is given in Cesare et al. (1992), where an integer between 1 and 7 is used to classify the state of the bridge upon inspection. A value of 1 indicates that the bridge is potentially hazardous and a value of 7 indicates that it is new. A sliding scale of deterioration then exists for the values in between. 3.6. Formation of an overall condition measure (Step 4) This stage of the framework proposes that it may be beneficial to combine the component condition measures identified in Step 3 to provide an overall item condition rating. The aim is to provide a condition measure which reflects the state of the item and which allows valid comparisons to be made between items of similar type. For the OCB example, as illustrated in Fig. 4, once component measures have been established for the condition of the Oil, Operating Mechanism and External part of an OCB, the condition of the breaker itself could then be represented by a vector of these three measurements. Alternatively, it may be desirable to combine the measures in some way so that an overall scalar index of degradation is formed. The motivation behind such a condition measure is that it will allow comparisons to be made between a number of items of the same type, in order to determine those in worse or better conditions. However, it will be necessary to have sufficient data detailing the condition of the three components coupled with suitable expert judgement in order to produce such a measure. At present such information is not widely available. Another example is a share index, where the actual condition of each company’s share value is combined to produce an overall measure for the state of the economy. Furthermore, an example taken from Juang and Amirkhanian (1992) involves measuring different types of cracking on a mile long road segment together with the severity of each type of crack. A fuzzy logic based approach was then used to combine these measures to give an overall degradation index for each mile long segment.
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3.7. Models describing the deterioration of the condition measure (Step 5)
Fig. 5.
and the severity of the environment in which it was installed. Alternatively the relationship may be of a non-uniform nature as shown in Fig. 6. It is important to establish the nature of this relationship and this is best achieved using any available engineering knowledge, such as national or international standards, coupled with a well thought out policy of data collection and analysis. Thomas (1996) lists different models that have been used in practice to model deterioration, including regression and Markov models. These models have a structure based upon certain (unknown) parameters which need to be determined from available data. Once a model has been selected data should also be used to validate the model.
Illustration of uniform degradation.
3.7.1. Structure of model It has been discussed during the explanation of Steps 3 and 4, how the condition measures of individual components, and hence of the overall condition rating of the item, will only be known intermittently. However, the values taken by these condition measures will change over the course of the item’s lifetime, and the manner in which such measures change needs to be determined. It is important to first consider what the main factors are that drive this change in item and component condition. Take as an example the condition of the oil in an OCB, and assume that this is adequately represented by the dielectric strength of the oil. This measure will only be known intermittently such as when maintenance is performed. In between such times, the oil condition will need to be estimated from less direct information which is available, such as the age of the oil. The number of switching operations performed is also likely to affect the oil condition, and if this were also available ‘Usage’ as well as ‘Age’ could be used to estimate the intermediate condition of the oil. Other influential factors may be the maintenance history of the item, its rating, location and so on. For example, suppose that the age of the OCB can be thought of as providing a direct relation to the dielectric strength of the oil and hence to the oil condition. The exact nature of the relationship between the factor ‘Age’ and the oil condition needs to be established, and a mathematical model can be used to describe this. One of the most common types of model used is that of a linear relationship as illustrated in Fig. 5. This type of model was used in Marshall (1997) to describe the deterioration of items of electrical equipment, in particular the components of Overhead Lines. New items were rated as having a 100% condition rating with the nominal replacement point being when the item reached a rating of 20%. The rate of deterioration was determined by factors based on the type of component
3.7.2. Regression models Regression models assume that the condition of an item (over time or usage, etc.) can be described by a curve or straight line, as seen in Liu et al. (1997), Jacobs (1992) and Marshall (1997). Such models are then used to predict the future condition of an item within an error margin, based upon the amount of variation within the data. Often these predictions will take the form of a confidence interval. 3.7.3. Markov models A Markov model, however, is a stochastic process which describes the future condition of an item by a collection of probabilities representing the chances of the item residing in each possible condition state. The condition states are usually indexed by integers (from 1 to N say), which represent worsening states of degradation. The exact number of states used will need to be determined from knowledge of the particular item or component and any available condition data. The underlying parameters of such models which describe the chances of moving between states are called transition probabilities. These transition probabilities can be obtained from a data set containing condition information and details of influential factors such as age or usage. Estimates of the underlying transition probabilities can then be obtained using methods of Maximum Likelihood, as explained in
Fig. 6.
Illustration of non-uniform degradation.
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Pugachev (1984), or Least Squares, an example being seen in Cesare et al. (1992). In practice, data detailing the dielectric strength of oil within oil filled switchgear and the associated ages of these samples have been obtained. It is shown in Hoskins et al. (1999) how a Markov model can be used to provide an adequate description of this data set, and to make predictions as to the future condition of the oil. 3.7.4. Modelling the effect of AM actions Having chosen a suitable model, it is then necessary to assess the changes in component condition that will be brought about by any particular maintenance action. This should be accomplished by giving careful examination to the exact nature of the maintenance operation and where possible supporting this with data. For instance, data recorded before and after maintenance have been performed. Some of the more common tasks undertaken during the maintenance of an OCB are the cleaning of the contacts and the replacement of the oil. It would be expected as a result that the condition of the operating mechanism and of the oil will improve. A complication is introduced however, because switching operations are a source of risk and a significant number of all switching operations are caused by the need to switch an item so that maintenance can be performed. Two methods have been observed in practice which account for the effect of maintenance, or other AM actions within such a model. Firstly, maintenance may be assumed to have a deterministic effect, such as to always return the item to its best condition, as seen in Barlow and Hunter (1960). An alternative assumption is that maintenance improves an item’s condition rating by a fixed amount, as seen in Liu et al. (1997). Secondly, stochastic models have been observed to describe the effect of maintenance. In particular, Markov models are able to incorporate a random element into maintenance activities reflecting the possibility that maintenance may not always be carried out to the same standard, and could even result in error, leaving the item in a worse condition. Predictions can be made of the number of network items in each state after maintenance under different AM actions. Thus, it is not surprising to find that Markov models have been successfully used in the management of both road and bridge networks, as outlined in Golabi et al. (1982) and Cesare et al. (1992). These face some similar problems to electricity networks, but had significant databases of condition measurements available. In practice a different condition model will be needed for each of the components that are identified in Step 3. For OCBs it should be the case that by changing the oil and the contacts on the interrupters at regular intervals it will be possible to ensure that the oil and operating mechanism are kept in a satisfactory condition
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over time. However, although some steps can be taken to prevent corrosion of the external part of the OCB, such as a new coat of paint, it would ultimately be expected that it will be the deterioration of this external component that drives the eventual replacement of the breaker. To accurately model the effect of such AM actions it will be necessary to combine any available condition measurements which describe the effect of maintenance with an expert analysis of what exactly maintenance involves, and its predicted effect upon component condition. Such predictions will then serve to highlight appropriate AM strategies. In practice it is not believed that such data exist within the UK ESI at present. Thus, initially engineering knowledge will be needed to predict the effect of different AM actions upon condition. However, such data are expected to become available in the future and can then be used in conjunction with engineering judgement. Hence, if a robust model can be found to describe the deterioration of each condition measure, degradation of the overall item condition follows as age, usage etc. increase and also as different maintenance actions are performed. This then allows the condition of a particular item or population to be predicted under different policies. 3.8. Analysis of different asset management policies (Step 6) If such condition models can be established to estimate the effect of ageing, usage, or available AM actions upon component condition, this provides the basis with which the questions being raised by utilities, as set out in Section 1.1, can be addressed. It would further be necessary to introduce the costs of the different AM actions, such as maintenance, inspection or replacement, and these will need to take into account manpower, component costs, outage costs, etc. The use of condition measures then allows the framework to be used as a tool for comparing different AM policies, by looking at the impact upon the future condition of the network. For instance, the impact of subjecting a network to a 10 yearly maintenance policy, might then be compared with that of a 15 yearly maintenance policy, in terms of cost and predicted future network condition. Other types of AM policy are discussed in Section 4.2, although any analysis may be subject to a number of constraints as set out in Section 4.3. The lack of availability of appropriate condition data and knowledge means that the ability to perform such an analysis for OCBs is at present a number of years away. However, preliminary quantitative applications of the framework to OCBs are outlined in Hoskins et al. (1999).
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4. Application Finally, some of the key practicalities involved with the implementation of this structure are now addressed. 4.1. Data requirements and inspections It is important to identify the practical data requirements of this modelling process, and it has been argued that because of the possible lack of failure data, AM policies should be based upon knowledge of the condition of items, together with available expert judgement. In general, condition information can be obtained from two sources. Firstly, by recording information when maintenance or other AM actions are performed, and secondly by making inspections upon a sample of the population. In the latter case, a number of issues will need to be addressed, such as the frequency of such inspections, the size of the samples, and determination of which items should be inspected. 4.2. Types of policy In practice, when determining an Asset Management policy there will only realistically be a limited number of policies that it is feasible to implement. The problem reduces to that of identifying the optimal policy within this set. The most common types of policy that have been observed in practice and that could feasibly be implemented are as follows. 4.2.1. Time Based Policies These have the general form that an item undergoes maintenance after a fixed number of years until it needs replacing. Time Based Maintenance Policies have been historically used in utilities world-wide. 4.2.2. Condition Based Policies These take the general form that an item is not maintained until the condition of the item reaches or exceeds a certain level (which could be a vector of component conditions). An example would be of an OCB which does not receive maintenance until the dielectric strength of the oil falls below a certain threshold, for instance 20 kV/mm. Realistically however, the condition will need to ascertained by carrying out some form of inspection. This leads to Inspect–Maintain Policies. 4.2.3. Inspect–Maintain Policies Effectively there will be a cost associated with the inspection required to determine the condition of an item or of its components. A decision is then taken upon the basis of this inspection as to whether or not to maintain the item. The most general form in which this policy can be expressed is:
“Inspect item every T years and maintain if the condition is found to be worse than C” If the proposed framework can be thoroughly implemented, and quantification of the different AM actions performed, it will then be possible to analyse and compare such different types of policy, in an effort to determine the most suitable policy. However, any practical implementation will take place in the presence of a number of constraints. 4.3. Constraints The most common forms of constraint encountered when considering AM policy are the following. 4.3.1. Budgetary constraints For instance in any particular year there may be an upper limit to size of the budget that can be allocated to maintenance and replacement. It may also be desirable that the yearly variation in this budget be kept to a minimum. 4.3.2. Manpower constraints This is similar to budgetary constraints but in this case it is acknowledged that there may be a limit to the number of maintenance operations that can feasibly be performed across the network in a single year. It should be possible to build these types of constraint into the above analysis by incorporating into the policies criteria of the form: “No more than, e.g. 15% of items can be maintained in a single year.” 4.3.3. State dependent constraints These might be called system performance constraints and refer to situations where restrictions are placed upon the proportion of items residing in a particular state at any time. These were seen in Golabi et al. (1982) where they took the general form: “Ensure at least x% of items are in the best state AND Ensure no more than y% of items are in the worst state” These are particularly applicable in Markov models where such state probabilities are provided by the model, as seen in Hoskins et al. (1999). Here, risk can be thought of as being the probability of residing in the worst condition state, and it might be insisted that at any one time this value never exceeds a predetermined threshold. 4.4. Need for periodic review of policy Once a particular policy has been decided upon, periodic reviews will need to be given to the different stages of the model. For example, as extra information is made available it is important that the degradation model is revalidated to ensure that it remains appropriate. This
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may lead to a cycle of validating then adjusting the model. Another factor is the advent of improved technology leading to safer and more reliable items. Thus, if an item is replaced it may be with an item of improved specification which requires a different model. This is particularly true for OCBs which nowadays are almost certain to be replaced by a vacuum or SF6 circuit breaker. Thus, a re-examination of policy should take place from time to time and reviews should also be given to the sampling and inspection policy. This was indicated in Fig. 3.
5. Conclusions Recently, concerns about the AM of network items have been raised in utilities world-wide, with several key issues being identified. Firstly, increasing attention is being paid to the selection of AM policy, with traditional time based policies being questioned, as condition based policies grow in favour. Secondly, because widescale replacement of network items will be required at some point and networks continue to age, it will be necessary to estimate when the end of life will be reached. A further concern is the attention that should be given to the inspection of items and the data recorded during such an inspection. These issues have been brought about by the increasing budgetary pressures within utilities coupled with the need to provide a service which attains regulatory targets of cost and safety. However, no clear techniques exist for addressing such issues. Theoretical and applied models are often difficult to implement in practice, because in many situations failure data are lacking. Such data are a requirement of these models. This paper begins to tackle this problem and to raise questions that need to be addressed. These are set out in the form of a general framework which aims to provide a structured approach to AM. In the absence of failure data it is proposed that condition information is used in conjunction with engineering knowledge concerning the onset of failure to formulate a model describing deterioration. The approach consists of six stages which attempt to identify the key issues associated with the implementation of AM. The primary area of concern has been the electricity distribution networks of the UK ESI, and in particular the AM of OCBs. Recent years have seen an increasing acknowledgement of the need to build condition databases and to use maintenance as a window of opportunity to obtain suitable condition measurements. Although the preliminary steps in the application of the framework to OCBs have been set out, there remain a considerable number of issues still to resolve. However, it is firmly believed that increasing use of condition information and models will be seen within the UK ESI as the industry continues to review its approach to AM.
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Development will continue, and this paper has aimed to set out some of the principal issues to be faced and to offer some guidelines within this area.
Acknowledgements This article is submitted by kind permission of the Director of R & D at EA Technology. The work was carried out under the UMIST/EA Technology Postgraduate Training Partnership, a joint DTI/EPSRC initiative, and was funded as part of EA Technology’s Strategic Technology Programme.
References Andrews, J.D., Moss, T.R., 1993. Reliability and Risk Assessment. Longman, Harlow, UK. Ansell, J., Phillips, M., 1994. Practical Methods for Reliability Data Analysis. Oxford University Press. Barlow, R., Hunter, L.C., 1960. Optimum preventive maintenance policies. Operations Research 8, 90–100. Billinton, R., Allan, R.N., 1996. Reliability Evaluation of Power Systems, 2nd ed. Plenum, New York/London. Bucci, R.M., Rebbapragada, R.V., McElroy, A.J., Chebli, E.A., Driller, S., 1994. Failure prediction of underground distribution feeder cables. IEEE Transactions on Power Delivery 9, 1943–1955. Cesare, M., Santamarina, C., Turkstra, C., Vanmarcke, E.H., 1992. Modelling bridge deterioration with Markov chains. ACSE Journal of Transportation Engineering 118 (6), 820–833. Cho, D.I., Parlar, M., 1991. A survey of maintenance models for multiunit systems. European Journal of Operational Research 51, 1–23. Douglas, R.L., Booth, R.A., 1992. Testing ageing switchgear. In: Proceedings of the Conference on Electric Energy, Brisbane, Australia, 19–21 October. Institute of Engineers, Australia. Duncan, D., O’Neill, D., 1995. Decision analysis ranks maintenance programs. Transmission and Distribution 47 (5), 14–19. Golabi, K., Kalkarni, R.B., Way, G.B., 1982. A state wide pavement management system. Interfaces 12 (6), 5–21. Harshbarger, S.L., Greenberg, V.A., 1994. Utility least cost planning and the Washington GAS integrated model. Computers and Operations Research 21 (6), 629–640. Hoskins, R.P., Strbac, G., Brint, A.T., 1999. Modelling the degradation of condition indices.IEE Proceedings—Generation, Transmission and Distribution in press. HSE, 1995. Oil-filled electrical distribution and other switchgear. HSE Information Document ID 483/27. Jacobs, T.L., 1992. Optimal long-term scheduling of bridge deck replacement and rehabilitation. ACSE Journal of Transportation Engineering 118 (2), 312–322. Juang, C.H., Amirkhanian, S.N., 1992. Unified Pavement Distress Index for managing flexible pavements. ACSE Journal of Transportation Engineering 118 (5), 686–699. Liu, C., Hammad, A., Itoh, Y., 1997. Maintenance strategy optimization of bridge decks using genetic algorithm. ACSE Journal of Transportation Engineering 123 (2), 91–100. Marshall, W., 1997. Software predicts future maintenance costs. Transmission and Distribution World March, 66–78. Nelson, W., 1992. Applied Life Data Analysis. Wiley, New York. O’Hagan, A., 1997. The ABLE story: Bayesian asset management in the water industry. Chapter 8 in: French, S., Smith, J.Q. (Eds.), The Practice of Bayesian Analysis. Arnold, London.
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Pierskalla, W.P., Voelker, J.A., 1976. A survey of maintenance models: the control and surveillance of deteriorating systems. Naval Research Logistics Quarterly 23, 353–388. Pintleton, L.M., Gelders, L., 1992. Maintenance management decision making. European Journal of Operational Research 58, 301–317. Pugachev, V.S., 1984. Probability Theory and Mathematical Statistics for Engineers. Pergamon Press, Oxford. Scherer, W.T., Glagola, D.M., 1994. Markovian models for bridge maintenance management. ACSE Journal of Transportation Engineering 120 (1), 37–51. Sherif, Y.S., Smith, M.L., 1981. Optimal maintenance models for sys-
tems subject to failure—a review. Naval Research Logistics Quarterly 28, 47–74. Thomas, L.C., 1996. Maintenance and measurability of degradation. In: Proceedings of 12th Advances in Reliability Technology Symposium (ARTS), Manchester, UK, 16–17 April. The Mechanical Reliability Committee of the Institution of Mechanical Engineers and The Safety and Reliability Society, London. Valdez-Flores, C., Feldman, R.M., 1989. A survey of preventive maintenance models for stochastically deteriorating single-unit systems. Naval Research Logistics Quarterly 36, 419–446.
A structured approach to Asset Management within the electricity industry R.P. Hoskins
a,c,*
, A.T. Brint b, G. Strbac
c
a EA Technology, Capenhurst, Chester, CH1 6ES, UK CORAS, Department of Computer and Mathematical Sciences, University of Salford, Salford M5 4WT, UK MCEE, Department of Electrical Engineering and Electronics, P.O. Box 88, UMIST, Manchester M60 1QD, UK b
c
Received 8 April 1998; received in revised form 20 October 1998; accepted 10 November 1998
Abstract Pressures are increasing upon utilities world-wide to provide a high quality of service at minimum cost, which requires a well founded Asset Management Policy to ensure network assets remain in a satisfactory condition. This issue is particularly acute in the UK Electricity Supply Industry where assets have attained high levels of reliability and failure data are limited, making Asset Management decisions difficult. By drawing upon examples observed in other industries a structured approach to Asset Management is developed. The approach contains six stages which are explained in detail together with a discussion of practical implementation. It is intended that the framework is sufficiently general that it can be applied to any industrial context, although the particular application to Oil-filled Circuit Breakers is detailed. 1999 Elsevier Science Ltd. All rights reserved. Keywords: Asset Management; Condition information; Maintenance modelling
1. Introduction 1.1. Outline of the problem Many utilities world-wide are presently operating within tightening regulatory environments, and are being forced to address the issues of providing a high quality of service to the customer at a cost deemed adequate by the regulatory body. This necessitates a sensible Asset Management (AM) policy, to ensure that the infrastructure remains in a satisfactory condition. In the past the emphasis was on expanding networks to securely connect whole populations, such as the electricity network or the water, road, gas networks, etc. This has been completed and has resulted in a shift in emphasis from the physical expansion of networks, to a focus on the quality of the service being delivered. Action is thus needed to prevent and counteract severe asset deterioration. However, in the increasingly competitive environment * Corresponding author, c/o Dr G. Strbac MCEE, Department of Electrical Engineering and Electronics, P.O. Box 88, UMIST, Manchester M60 1QD, UK. Tel.: ⫹ 44-0161-200-4803; e-mail: c/o [email protected]
in which the owners of such networks are operating, be they private or public companies, the budgets allocated towards the upkeep of the network are coming under increasing pressure, in an effort to operate at minimum cost. This budget accounts for the inspection, maintenance, refurbishment, replacement, etc. of network items, and is often termed Asset Management. Considerable attention is now being paid to the formulation of AM policy. It is important that the desire to minimise running costs is not at the expense of the investment needed to retain a satisfactory network condition. Failure to invest sufficiently in the maintenance and replacement of such networks could have safety implications, as this may result in items becoming exposed to a larger risk of failure. Such a failure could result in serious injury or even fatality in some cases. Furthermore, it may be the case that the impact of a deferral or reduction in investment is not immediately noticed, but that such a decision leads to considerable increases in the necessary expenditure in future years. This might be brought about by the enforced replacement of items at an earlier time than if they had been maintained more frequently. This possibility that the implications of a reduction in costs may not manifest themselves for a number of years,
0957-1787/99/$ - see front matter 1999 Elsevier Science Ltd. All rights reserved. PII: S 0 9 5 7 - 1 7 8 7 ( 9 8 ) 0 0 0 1 5 - 0
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makes decisions concerning AM policy particularly difficult. The range of questions that utility owners have identified and are trying to produce answers to include the following. 1. What is the impact upon risk and cost in extending the interval of a time based maintenance policy? For instance from a 10 year maintenance interval to a 12 year one? 2. At what condition should an item be maintained to ensure that it retains an adequate level of safety? 3. How often should a given device be inspected? Furthermore, what information should be recorded when carrying out such an inspection, and how should the information be exploited for business purposes? 4. When are networks going to require replacement and how should large scale replacement be implemented? 5. What is the variation in cost between the different years of a given Asset Management policy? Furthermore, would a slightly more expensive policy be considered if it ensured that the budget remained more constant from year to year? There is thus a strong need for a thorough approach to making AM policy decisions and determining the associated budget. This paper proposes a comprehensive structure in which suitable quantitative information is combined with a physical model to allow these issues to be addressed. The result is a mathematical model of degradation which can be established in both situations where substantial failure data exist, and those where there is a sparsity of failure data. The advantage of such a degradation model is that it allows predictions to be made as to the future condition of the network and of the impact of different AM actions. This is particularly useful when failure data are lacking and failure rate models are difficult to establish, since the acceptability of different future network conditions can then be used to influence the choice of AM policy. Discussion is given of the use of Markov models to model degradation and these have been applied successfully in transportation and other industries to provide a more thorough quantification of AM decisions. It is intended that the proposed structure is sufficiently general that it can be applied to any particular industrial context, to provide solutions to the above questions. The following subsection describes the specific aims of AM within the UK ESI, with emphasis on the particular difficulties faced and the differences from other industries. This is followed by a discussion of the AM practices observed in various utilities and a critical assessment of what is needed in general. The third section of this paper proposes the general framework for making AM decisions, and explains in detail the specific nature of the different stages of this framework. Follow-
ing this, a discussion is given of the practical implementation of such a framework, and finally the conclusions to be drawn are considered. 1.2. United Kingdom Electricity Supply Industry The motivations behind this paper are the particular AM concerns of the distribution network of the UK Electricity Supply Industry (ESI). This is owned by the Electricity Companies, who are responsible for the operation and upkeep of the network. This consists primarily of four main types of asset: Overhead Lines, Underground Cables, Transformers and Switchgear. The bulk of the network was constructed in the late 1950s and early 1960s and concern is growing as to how the assets should be managed. The main reasons for this increased concern are as follows. 쐌 Privatisation and restructuring of the ESI. This occurred in the UK in 1990 and has since led to increased pressure on budgets. It was observed in Duncan and O’Neill (1995) that “In the battle of the budget Preventive Maintenance is a tempting place to cut since the effects may not be felt for years”. Furthermore, it is necessary for the proposed capital investment of the electricity companies to be accepted by the regulator, who then allows the costs to be passed on to the customer. The external regulator will need convincing that the size of such budgets are determined by a consistent and thorough understanding of the state of the network assets. 쐌 Increasing age of the network. Although the majority of the network is approaching an age of 40 years, some parts of the electricity network are presently over 100 years old. Hence, there is a real need to ensure that the network remains in a satisfactory condition. 쐌 Quality of supply. During past decades when the network was expanding, the principal concern of the ESI was to ensure that all customers were connected, and that the supply of electricity met the consumers’ demand. Nowadays this is rarely an issue and attention has shifted to address questions of quality rather than quantity. Such quality issues include customer interruptions, restoration times and power quality (avoidance of voltage sags, harmonics, etc.) Furthermore, the running costs of the network have also come under the spotlight. To ensure that these factors attain satisfactory targets as set by the regulator, it will be necessary to have a well managed network in operation. 쐌 Catastrophic failures. Though extremely rare these incidents can lead to fatality and serious injury, and hence policy needs to ensure that such failures are avoided. It is essential within the ESI that AM decisions take into
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account the major factors present within the industry. These are: 쐌 High reliability. Most equipment has been installed on the network for a considerable time and has been subject to a preventive maintenance policy. This has resulted in very few failures occurring. The lack of failure data causes difficulty in making inferences about the expected life of an asset and in determining AM policy. 쐌 Safety critical issues. When failures do occur, however, there is the potential for catastrophe. This is particularly true of older switchgear which is filled with oil. Attention will be paid to the formulation of AM policies for Oil-filled Circuit Breakers (OCBs), which are difficult items to deal with, because the above two factors are particularly relevant. Since their installation, these items have failed very infrequently, but some of these failures have led to fatalities. Coupled with the fact that thousands of these items are in operation, many of which are currently approaching the end of their often touted 40 year book or design life, the AM of such items is a major concern of the UK ESI at present.
2. Overview of approaches to AM 2.1. AM within the UK ESI Network items have traditionally been maintained on a fixed time interval basis, that is, the time between successive maintenance operations is constant and usually does not vary between items of the same type. The length of this interval, as Douglas and Booth (1992) observe, is determined by an ad hoc approach combining manufacturer’s recommendations, the experience gained from operating the item, and engineering judgement. Such an interval has tended to be around 10 years, although it does appear that maintenance intervals have been extended in the recent past. In conjunction, a book life is often used to determine the age at which items should be replaced, and this is often around 35–40 years. For some items of equipment in the industry, particularly oil filled switchgear, the purpose of performing maintenance has been primarily to prevent catastrophes from occurring. Thus, although a deferral of maintenance might eventually necessitate an earlier replacement of the item, the real driver is that of safety and to avoid the item being exposed to an increased risk. 2.2. AM within other industries In the present climate, business drivers to reduce costs exist even in publicly owned companies, and the problems outlined in Section 1.1 are faced in most industries
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to a greater or lesser extent. However, the factors of high reliability and safety are not always present within other industries, where different factors may exist. For instance, in the manufacturing industry, failures are usually much more common and do not pose as great a problem to safety. Furthermore, such an environment is often considerably more constrained than the electricity distribution (and transmission) network, due to items being located indoors and over a significantly smaller area. Hence, there is often much more knowledge available about the condition of such items. This results in a much better understanding of the behaviour of such items, and allows the performance of the item throughout its life-cycle to be determined more accurately. A more thorough and detailed formulation of AM policy can then be carried out. Situations similar to that of the UK ESI have been observed in the management of US bridges and roads (often termed pavements in the US). These parts of stateowned infrastructure must address whether postponement of maintenance will lead to a substantial increase in the necessary repair bill in future years. In these industries such problems are often targeted by a Bridge Management System (BMS) or a Pavement Management System (PMS). These are medium to long term plans which describe the expected needs of the infrastructure and look at policy optimisation to ensure that such needs are met in a cost effective manner. It is explained in Scherer and Glagola (1994) how the need for a systematic approach to maintenance and rehabilitation strategies within the transportation system was not identified in the US until the late 1960s. This was mainly provoked by a catastrophic incident involving the collapse of a bridge in 1967. The subsequent Highway Safety Act of 1968 required state highway officials to inspect and rate bridges within their jurisdiction. This led many states to formulate a computational approach to maintenance in the form of a BMS. A BMS is defined in Scherer and Glagola (1994) to be “A rational and systematic approach to organising and carrying out all the activities related to managing a network of bridges. It includes optimising the selection of maintenance and improvement actions to maximise the benefits while minimising the costs.” Since 1993, each US state has been required to have implemented a PMS which was described in Juang and Amirkhanian (1992) as a system involving the “collection and manipulation of data in order to generate information on which rational decisions can be based”. Furthermore, it was described in Golabi et al. (1982) that in the past, roads had been allowed to deteriorate too severely before action was taken, but that when repairs were carried out too much remedial work was performed. Introduction of the PMS and BMS has led to a more thorough understanding of the condition of the net-
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work assets and allowed an examination of different maintenance policies and their associated costs. Furthermore, the US natural gas and electric utilities also operate in a regulatory environment and are required by legislation to construct long term plans detailing network management. Such legislation is outlined in the 1992 Energy Policy Act and is explained in Harshbarger and Greenberg (1994). This states the requirement of each publicly regulated electric utility to “provide adequate and reliable service to its electric customers at the lowest system cost”. The publicly regulated natural gas utilities are required to “minimise life-cycle costs of adequate and reliable utility services to gas consumers”. These requirements are often achieved using the strategies of Least Cost Planning or Integrated Resource Planning as outlined in Harshbarger and Greenberg (1994). This is similar in the water authorities of England and Wales who must submit what is titled an Asset Management Plan to the regulatory body, the Office of Water Services (OFWAT), at regular intervals. This forecasts the condition of the assets the company owns and the costs of necessary maintenance and renewal over a 20 year period, amongst other information. This is explained in O’Hagan (1997). Thus in many utilities, a clear goal has been identified to produce thorough answers to the questions identified in Section 1.1. 2.3. Critical assessment of existing AM policy The traditional time based maintenance policy coupled with a fixed book life approach that has been widely used within the ESI appears to have several shortcomings. 1. It is not clear that such policies are optimal. Although time based policies have been implemented within the industry for a large number of years, there does not appear to be any firm justification as to why they are the optimal type of maintenance policy. A discussion of other possible policy types is given in Section 4.2. 2. It is widely felt nowadays that such policies may be too conservative and that many individual items could have maintenance considerably delayed, or even abolished. However, because of the risks involved a firm justification is needed before this can be carried out. 3. The traditional approach does not provide clear predictions as to the future condition of the network items, or as to when their replacement will be required. It is particularly difficult to firmly justify AM policy for OCBs because of the lack of available data, and the need to avoid catastrophic failure. The solutions that have been observed in other industries, such as the PMS
and BMS, have relied heavily on the existence of a large database of information about the condition of network assets. Furthermore, in other industries the safety factor is often not as critical. For OCBs it is believed that the safety implications dominate the choice of AM policy, and this is emphasised in the HSE recommendations (HSE, 1995). For other items of electrical equipment, and in other industries where the possibility of catastrophe is not as high, maintenance is primarily performed to avoid future increases in cost. Thus, the five general questions posed within Section 1.1 will have differing degrees of importance, depending upon the particular industrial context being considered. However, such questions cannot always be directly addressed using existing methods.
2.4. Analysis of situation
The development of a general framework to provide answers to these questions is the basis of this paper. At the heart of this framework is the need for quantification and modelling of the condition of an item and an understanding of the costs and benefits of available AM actions. Although condition information may not be freely available, it can be obtained with some expenditure, and in the absence of failure data this is imperative. Obtaining suitable condition information may require inspections of plant at various intervals, and the recording of relevant measurements. The frequency and scope of such inspections will need to be determined by balancing the costs of carrying them out with the amount of information required to accurately model AM policy. Within the electricity industry, very large items of transmission and generation plant often have condition monitoring performed on-line in order to provide information as to the state of the item, but this is not financially viable at distribution levels. The National Fault and Interruption Reporting Scheme (NAFIRS) was set up in 1965 with the aim of monitoring the performance of electrical items. However, this only records faults and was not intended to be used as a basis for influencing AM policy. Further, it has been revised on a number of occasions and thus it is difficult to use the NAFIRS database to make informed AM decisions. In summary, it is strongly believed that in the absence of data concerning the failure of items, decisions of Asset Management must take into account other sources of information about the state of the network. In particular, data representing the condition of specific items on the network should allow an understanding to be gained of how items perform and deteriorate over time.
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3. Proposed AM framework
Fig. 1.
Traditional failure rate model.
3.1. Modelling of maintenance policies A considerable number of theoretical mathematical models which describe the effect of maintenance upon an item exist, and comprehensive overviews of these can be found in Pierskalla and Voelker (1976), Sherif and Smith (1981), Valdez-Flores and Feldman (1989), or Cho and Parlar (1991). One of the earliest such models was proposed in Barlow and Hunter (1960) where it is assumed that a maintenance action restores an item to its initial condition. By then introducing the costs of maintenance and replacement, together with the failure rate of an item, it is shown how the optimal time interval between maintenance operations can be found. Many other models extend this to look at incorporating into the modelling process, for instance, the use of inspections, minimisation of downtime, the availability of spare parts, amongst many others. However, models which describe maintenance usually rely heavily on the availability of the failure rate function of the item over time, such as the bathtub curve model described in Billinton and Allan (1996). Such a function is used to determine the best maintenance and replacement actions by optimising expected costs over the planning time horizon. When this approach is feasible, the life of an item will usually be represented by a probability distribution. This distribution will often be constructed from records of previous failures, and techniques for doing so are outlined in Nelson (1992) and in Ansell and Phillips (1994). In effect, failure rate models attempt to uncover a direct link between the age of an item and its probability of failure, which can be regarded as the risk to which the item is exposed at a particular time. This approach is illustrated in Fig. 1. However, in the UK ESI and other utilities, the lack of failure data rules out such an approach. This causes difficulties as emphasised by Pintleton and Gelders (1992), who note that “most maintenance optimisation models presume availability of data. In many cases suitable and correct data are not available.” Thus, an alternative approach is needed.
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3.2. Overview of proposed AM structure The types of failure that an item may suffer can range from minor breakdown requiring only a short time to correct to a full blown explosion of an asset which would necessitate an in-depth investigation and replacement of the whole asset. Thus, it is believed important to be aware of the types of failure that Asset Management policy aims to prevent. Furthermore, in order that such types of failure can be targeted it is thought necessary to possess an understanding of the physical process by which such failures are brought about. This should be determined from expertise and should aim to identify the item components whose deterioration can lead to the targeted types of failure. The failure distribution of the item may be very difficult to determine, and so it is proposed to construct a measure of item condition, as an alternative to the failure probability. To ascertain the condition of an item often requires an inspection, but in the interval between inspections the exact condition will not be known. To overcome this difficulty, a model is proposed to provide estimates of condition during this interval. The model will base the condition of the item upon such factors as its age, usage, location, condition at the last inspection, and so on. In cases where failure data are available, it may then be possible to estimate a measure of risk or failure rate which is related to the condition of the item. The concepts of measures of condition and risk are illustrated in Fig. 2. Where failure data are lacking, such as for a number of items of equipment within the UK ESI, it is unlikely that such a risk measure can be established, other than through elicitation of expert engineering judgement and possibly laboratory tests. In practice it may be the case that as an alternative to a robust risk function, a bound upon the condition measure is used, to characterise the threshold above which the risk is deemed to be unacceptable. Condition information is available and once obtained can be used to determine an appropriate condition function. Once a robust condition function has been determined, predictions can be made of the future behaviour of the item (or technically of its condition (measure)). Examin-
Fig. 2.
Proposed failure rate model.
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ation of the tasks involved in different AM actions, and any available data, may then allow the effect of such actions upon item and component condition to be estimated. This enables predictions of the future condition of an item when different AM policies are implemented. Furthermore, introduction of the costs of the different maintenance actions allows comparisons of different AM policies. A decision can then be taken as to the most suitable AM policy in terms of costs and future network condition. The structure of this proposed methodology is summarised in Fig. 3. The principal drivers of this approach are data relating to the condition of items and components. It is these condition data that are used to supplement the lack of failure data. This approach is characterised by six distinct stages which are discussed together with the application to the particular problem of OCBs. 3.3. Identification of most important failure modes (Step 1) It is common for items to be able to fail in a number of different ways each having a different set of consequences. These are often termed failure modes, and it is essential to identify the failure modes of the greatest consequence that need to be avoided. Asset Management policy should then be targeted at preventing these failure modes.
Fig. 3.
Illustration of overview of proposed method.
For OCBs, as previously stated, the principal aim of AM is to avoid catastrophic failure. 3.4. Identification of components and their relative importance (Step 2) In order to prevent failures occurring it is of utmost importance to understand the mechanism by which failures are brought about. Techniques such as Failure Modes and Effects Analysis (FMEA) and Fault Tree Analysis (FTA), outlined in Andrews and Moss (1993), provide such a method. In particular, FTA aims to describe the failure of an item through the identification of the combinations of component failures, that result in a particular failure mode of the item being realised. The item is viewed as a collection of different components which are physically combined in a particular manner. By linking the component failures through a series of AND and OR gates, the individual component failure probabilities are combined to determine the ‘top event’ probability (i.e. item failure). In the case of OCBs, there is a lack of failure data, and hence the component failure probabilities are very small and are difficult to estimate. Therefore, the components which may contribute to the catastrophic failure of an OCB have been identified, and rather than attempting an extremely detailed modelling involving small probabilities, component condition measures are used as an alternative. The schematic representation of such failures is shown in Fig. 4 and is intended to provide a simplistic view of how catastrophe may be brought about. For example, it might be the case that the arc formed when the OCB opens is not extinguished in the required time, possibly because of the operating mechanism not performing correctly. This could be due to the contacts being drawn apart too slowly (possibly as a result of a weakened spring mechanism), the contacts only opening partially and then getting stuck, or as a result of the contacts being corroded or containing deposits such as carbon. Backup protection should then operate but it is possible that the persistence of the arc will ignite the oil and lead to an explosion. From the investigation, the principal components whose deterioration could contribute towards a catastrophic failure have been identified as the Oil, the External Condition, and the Operating Mechanism. As a result, it is recommended that efforts should be made to obtain information detailing the condition of these three components. By way of example, Bucci et al. (1994) describes how underground cables in New York have been analysed by looking at the individual reliabilities of their constituent components—cable sections, cable joints and network transformers. A second example from Marshall (1997) describes how in measuring the condition of items on the electricity network, Overhead Lines were divided
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Fig. 4.
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Failure process of an OCB.
into the component groups Insulator Fitting, Tower Steel or Conductor. 3.5. Construction of suitable component measures (Step 3) This stage prescribes that condition measures should now be established for those components identified in Step 2. If failure data are available, the failure rate of each component identified in Step 2 can be used to provide a measure of the condition of each component. However, because such failure data are often not available, an alternative measure of condition may be required, together with relevant condition data. An assessment is needed of what constitutes a good measure of a component’s condition, and great importance must be placed on ensuring that the data used to describe this component measure, are of good quality. For instance, consider the choice of condition measure for the oil within an OCB. Several different measures have been observed, for example, the dielectric strength (measured in kV/mm), dissolved moisture content (measured in ppm), or the oil colour (on a scale of 1– 10). These component conditions will only be known when inspection or maintenance has been carried out. During intermediary periods a model is used to provide estimates of condition, as detailed in Step 5 in Section 3.7. However, at present such condition information is not widely available. It is believed that UK electricity companies have begun to see the value in collecting these data, and are starting to implement condition databases. Thus, it is anticipated that such information will become available in the future. A very common example outside of the ESI is that of motor vehicles. It is common when these are traded to quote the age and the mileage of the item, which enables an initial estimate of the vehicle condition to be made. However, more specific information such as the state of the engine or the amount of rust necessitates a detailed inspection. Furthermore, situations have been observed where instead of using a directly measurable quantity to represent condition, a measure is constructed based on a
more human perception. An example taken from Bridge Management is given in Cesare et al. (1992), where an integer between 1 and 7 is used to classify the state of the bridge upon inspection. A value of 1 indicates that the bridge is potentially hazardous and a value of 7 indicates that it is new. A sliding scale of deterioration then exists for the values in between. 3.6. Formation of an overall condition measure (Step 4) This stage of the framework proposes that it may be beneficial to combine the component condition measures identified in Step 3 to provide an overall item condition rating. The aim is to provide a condition measure which reflects the state of the item and which allows valid comparisons to be made between items of similar type. For the OCB example, as illustrated in Fig. 4, once component measures have been established for the condition of the Oil, Operating Mechanism and External part of an OCB, the condition of the breaker itself could then be represented by a vector of these three measurements. Alternatively, it may be desirable to combine the measures in some way so that an overall scalar index of degradation is formed. The motivation behind such a condition measure is that it will allow comparisons to be made between a number of items of the same type, in order to determine those in worse or better conditions. However, it will be necessary to have sufficient data detailing the condition of the three components coupled with suitable expert judgement in order to produce such a measure. At present such information is not widely available. Another example is a share index, where the actual condition of each company’s share value is combined to produce an overall measure for the state of the economy. Furthermore, an example taken from Juang and Amirkhanian (1992) involves measuring different types of cracking on a mile long road segment together with the severity of each type of crack. A fuzzy logic based approach was then used to combine these measures to give an overall degradation index for each mile long segment.
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3.7. Models describing the deterioration of the condition measure (Step 5)
Fig. 5.
and the severity of the environment in which it was installed. Alternatively the relationship may be of a non-uniform nature as shown in Fig. 6. It is important to establish the nature of this relationship and this is best achieved using any available engineering knowledge, such as national or international standards, coupled with a well thought out policy of data collection and analysis. Thomas (1996) lists different models that have been used in practice to model deterioration, including regression and Markov models. These models have a structure based upon certain (unknown) parameters which need to be determined from available data. Once a model has been selected data should also be used to validate the model.
Illustration of uniform degradation.
3.7.1. Structure of model It has been discussed during the explanation of Steps 3 and 4, how the condition measures of individual components, and hence of the overall condition rating of the item, will only be known intermittently. However, the values taken by these condition measures will change over the course of the item’s lifetime, and the manner in which such measures change needs to be determined. It is important to first consider what the main factors are that drive this change in item and component condition. Take as an example the condition of the oil in an OCB, and assume that this is adequately represented by the dielectric strength of the oil. This measure will only be known intermittently such as when maintenance is performed. In between such times, the oil condition will need to be estimated from less direct information which is available, such as the age of the oil. The number of switching operations performed is also likely to affect the oil condition, and if this were also available ‘Usage’ as well as ‘Age’ could be used to estimate the intermediate condition of the oil. Other influential factors may be the maintenance history of the item, its rating, location and so on. For example, suppose that the age of the OCB can be thought of as providing a direct relation to the dielectric strength of the oil and hence to the oil condition. The exact nature of the relationship between the factor ‘Age’ and the oil condition needs to be established, and a mathematical model can be used to describe this. One of the most common types of model used is that of a linear relationship as illustrated in Fig. 5. This type of model was used in Marshall (1997) to describe the deterioration of items of electrical equipment, in particular the components of Overhead Lines. New items were rated as having a 100% condition rating with the nominal replacement point being when the item reached a rating of 20%. The rate of deterioration was determined by factors based on the type of component
3.7.2. Regression models Regression models assume that the condition of an item (over time or usage, etc.) can be described by a curve or straight line, as seen in Liu et al. (1997), Jacobs (1992) and Marshall (1997). Such models are then used to predict the future condition of an item within an error margin, based upon the amount of variation within the data. Often these predictions will take the form of a confidence interval. 3.7.3. Markov models A Markov model, however, is a stochastic process which describes the future condition of an item by a collection of probabilities representing the chances of the item residing in each possible condition state. The condition states are usually indexed by integers (from 1 to N say), which represent worsening states of degradation. The exact number of states used will need to be determined from knowledge of the particular item or component and any available condition data. The underlying parameters of such models which describe the chances of moving between states are called transition probabilities. These transition probabilities can be obtained from a data set containing condition information and details of influential factors such as age or usage. Estimates of the underlying transition probabilities can then be obtained using methods of Maximum Likelihood, as explained in
Fig. 6.
Illustration of non-uniform degradation.
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Pugachev (1984), or Least Squares, an example being seen in Cesare et al. (1992). In practice, data detailing the dielectric strength of oil within oil filled switchgear and the associated ages of these samples have been obtained. It is shown in Hoskins et al. (1999) how a Markov model can be used to provide an adequate description of this data set, and to make predictions as to the future condition of the oil. 3.7.4. Modelling the effect of AM actions Having chosen a suitable model, it is then necessary to assess the changes in component condition that will be brought about by any particular maintenance action. This should be accomplished by giving careful examination to the exact nature of the maintenance operation and where possible supporting this with data. For instance, data recorded before and after maintenance have been performed. Some of the more common tasks undertaken during the maintenance of an OCB are the cleaning of the contacts and the replacement of the oil. It would be expected as a result that the condition of the operating mechanism and of the oil will improve. A complication is introduced however, because switching operations are a source of risk and a significant number of all switching operations are caused by the need to switch an item so that maintenance can be performed. Two methods have been observed in practice which account for the effect of maintenance, or other AM actions within such a model. Firstly, maintenance may be assumed to have a deterministic effect, such as to always return the item to its best condition, as seen in Barlow and Hunter (1960). An alternative assumption is that maintenance improves an item’s condition rating by a fixed amount, as seen in Liu et al. (1997). Secondly, stochastic models have been observed to describe the effect of maintenance. In particular, Markov models are able to incorporate a random element into maintenance activities reflecting the possibility that maintenance may not always be carried out to the same standard, and could even result in error, leaving the item in a worse condition. Predictions can be made of the number of network items in each state after maintenance under different AM actions. Thus, it is not surprising to find that Markov models have been successfully used in the management of both road and bridge networks, as outlined in Golabi et al. (1982) and Cesare et al. (1992). These face some similar problems to electricity networks, but had significant databases of condition measurements available. In practice a different condition model will be needed for each of the components that are identified in Step 3. For OCBs it should be the case that by changing the oil and the contacts on the interrupters at regular intervals it will be possible to ensure that the oil and operating mechanism are kept in a satisfactory condition
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over time. However, although some steps can be taken to prevent corrosion of the external part of the OCB, such as a new coat of paint, it would ultimately be expected that it will be the deterioration of this external component that drives the eventual replacement of the breaker. To accurately model the effect of such AM actions it will be necessary to combine any available condition measurements which describe the effect of maintenance with an expert analysis of what exactly maintenance involves, and its predicted effect upon component condition. Such predictions will then serve to highlight appropriate AM strategies. In practice it is not believed that such data exist within the UK ESI at present. Thus, initially engineering knowledge will be needed to predict the effect of different AM actions upon condition. However, such data are expected to become available in the future and can then be used in conjunction with engineering judgement. Hence, if a robust model can be found to describe the deterioration of each condition measure, degradation of the overall item condition follows as age, usage etc. increase and also as different maintenance actions are performed. This then allows the condition of a particular item or population to be predicted under different policies. 3.8. Analysis of different asset management policies (Step 6) If such condition models can be established to estimate the effect of ageing, usage, or available AM actions upon component condition, this provides the basis with which the questions being raised by utilities, as set out in Section 1.1, can be addressed. It would further be necessary to introduce the costs of the different AM actions, such as maintenance, inspection or replacement, and these will need to take into account manpower, component costs, outage costs, etc. The use of condition measures then allows the framework to be used as a tool for comparing different AM policies, by looking at the impact upon the future condition of the network. For instance, the impact of subjecting a network to a 10 yearly maintenance policy, might then be compared with that of a 15 yearly maintenance policy, in terms of cost and predicted future network condition. Other types of AM policy are discussed in Section 4.2, although any analysis may be subject to a number of constraints as set out in Section 4.3. The lack of availability of appropriate condition data and knowledge means that the ability to perform such an analysis for OCBs is at present a number of years away. However, preliminary quantitative applications of the framework to OCBs are outlined in Hoskins et al. (1999).
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4. Application Finally, some of the key practicalities involved with the implementation of this structure are now addressed. 4.1. Data requirements and inspections It is important to identify the practical data requirements of this modelling process, and it has been argued that because of the possible lack of failure data, AM policies should be based upon knowledge of the condition of items, together with available expert judgement. In general, condition information can be obtained from two sources. Firstly, by recording information when maintenance or other AM actions are performed, and secondly by making inspections upon a sample of the population. In the latter case, a number of issues will need to be addressed, such as the frequency of such inspections, the size of the samples, and determination of which items should be inspected. 4.2. Types of policy In practice, when determining an Asset Management policy there will only realistically be a limited number of policies that it is feasible to implement. The problem reduces to that of identifying the optimal policy within this set. The most common types of policy that have been observed in practice and that could feasibly be implemented are as follows. 4.2.1. Time Based Policies These have the general form that an item undergoes maintenance after a fixed number of years until it needs replacing. Time Based Maintenance Policies have been historically used in utilities world-wide. 4.2.2. Condition Based Policies These take the general form that an item is not maintained until the condition of the item reaches or exceeds a certain level (which could be a vector of component conditions). An example would be of an OCB which does not receive maintenance until the dielectric strength of the oil falls below a certain threshold, for instance 20 kV/mm. Realistically however, the condition will need to ascertained by carrying out some form of inspection. This leads to Inspect–Maintain Policies. 4.2.3. Inspect–Maintain Policies Effectively there will be a cost associated with the inspection required to determine the condition of an item or of its components. A decision is then taken upon the basis of this inspection as to whether or not to maintain the item. The most general form in which this policy can be expressed is:
“Inspect item every T years and maintain if the condition is found to be worse than C” If the proposed framework can be thoroughly implemented, and quantification of the different AM actions performed, it will then be possible to analyse and compare such different types of policy, in an effort to determine the most suitable policy. However, any practical implementation will take place in the presence of a number of constraints. 4.3. Constraints The most common forms of constraint encountered when considering AM policy are the following. 4.3.1. Budgetary constraints For instance in any particular year there may be an upper limit to size of the budget that can be allocated to maintenance and replacement. It may also be desirable that the yearly variation in this budget be kept to a minimum. 4.3.2. Manpower constraints This is similar to budgetary constraints but in this case it is acknowledged that there may be a limit to the number of maintenance operations that can feasibly be performed across the network in a single year. It should be possible to build these types of constraint into the above analysis by incorporating into the policies criteria of the form: “No more than, e.g. 15% of items can be maintained in a single year.” 4.3.3. State dependent constraints These might be called system performance constraints and refer to situations where restrictions are placed upon the proportion of items residing in a particular state at any time. These were seen in Golabi et al. (1982) where they took the general form: “Ensure at least x% of items are in the best state AND Ensure no more than y% of items are in the worst state” These are particularly applicable in Markov models where such state probabilities are provided by the model, as seen in Hoskins et al. (1999). Here, risk can be thought of as being the probability of residing in the worst condition state, and it might be insisted that at any one time this value never exceeds a predetermined threshold. 4.4. Need for periodic review of policy Once a particular policy has been decided upon, periodic reviews will need to be given to the different stages of the model. For example, as extra information is made available it is important that the degradation model is revalidated to ensure that it remains appropriate. This
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may lead to a cycle of validating then adjusting the model. Another factor is the advent of improved technology leading to safer and more reliable items. Thus, if an item is replaced it may be with an item of improved specification which requires a different model. This is particularly true for OCBs which nowadays are almost certain to be replaced by a vacuum or SF6 circuit breaker. Thus, a re-examination of policy should take place from time to time and reviews should also be given to the sampling and inspection policy. This was indicated in Fig. 3.
5. Conclusions Recently, concerns about the AM of network items have been raised in utilities world-wide, with several key issues being identified. Firstly, increasing attention is being paid to the selection of AM policy, with traditional time based policies being questioned, as condition based policies grow in favour. Secondly, because widescale replacement of network items will be required at some point and networks continue to age, it will be necessary to estimate when the end of life will be reached. A further concern is the attention that should be given to the inspection of items and the data recorded during such an inspection. These issues have been brought about by the increasing budgetary pressures within utilities coupled with the need to provide a service which attains regulatory targets of cost and safety. However, no clear techniques exist for addressing such issues. Theoretical and applied models are often difficult to implement in practice, because in many situations failure data are lacking. Such data are a requirement of these models. This paper begins to tackle this problem and to raise questions that need to be addressed. These are set out in the form of a general framework which aims to provide a structured approach to AM. In the absence of failure data it is proposed that condition information is used in conjunction with engineering knowledge concerning the onset of failure to formulate a model describing deterioration. The approach consists of six stages which attempt to identify the key issues associated with the implementation of AM. The primary area of concern has been the electricity distribution networks of the UK ESI, and in particular the AM of OCBs. Recent years have seen an increasing acknowledgement of the need to build condition databases and to use maintenance as a window of opportunity to obtain suitable condition measurements. Although the preliminary steps in the application of the framework to OCBs have been set out, there remain a considerable number of issues still to resolve. However, it is firmly believed that increasing use of condition information and models will be seen within the UK ESI as the industry continues to review its approach to AM.
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Development will continue, and this paper has aimed to set out some of the principal issues to be faced and to offer some guidelines within this area.
Acknowledgements This article is submitted by kind permission of the Director of R & D at EA Technology. The work was carried out under the UMIST/EA Technology Postgraduate Training Partnership, a joint DTI/EPSRC initiative, and was funded as part of EA Technology’s Strategic Technology Programme.
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