Integrated framework for behaviour analysis in a process plant

Accepted Manuscript Integrated Framework for Behaviour Analysis in a Process plant Dilbagh Panchal, Dinesh Kumar, Prof. PII:

S0950-4230(15)30099-1

DOI:

10.1016/j.jlp.2015.12.021

Reference:

JLPP 3109

To appear in:

Journal of Loss Prevention in the Process Industries

Received Date: 18 September 2015 Revised Date:

22 December 2015

Accepted Date: 25 December 2015

Please cite this article as: Panchal, D., Kumar, D., Integrated Framework for Behaviour Analysis in a Process plant, Journal of Loss Prevention in the Process Industries (2016), doi: 10.1016/ j.jlp.2015.12.021. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Maintenance expert opinion

Phase 1 Reliability analysis

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Data collection sources

Phase 1 Data collection

Maintenance log book

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Compute reliability parameters using fuzzy λ-τ approach

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System reliability analysis

Root Cause Analysis Phase 2 Risk analysis

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Failure Mode Effect Analysis

Fuzzy Decision Making System

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Grey Relation Analysis

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Component risk ranking comparison System risk analysis

Graphical abstract

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Integrated Framework for Behaviour Analysis in a Process

Dilbagh Panchal1, Dinesh Kumar2 1

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plant

Research scholar, Department of Mechanical and Industrial Engineering, Indian institute of

2

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Technology, Roorkee, Uttarakhand, India-247667

Prof., Department of Mechanical and Industrial Engineering, Indian Institute of Technology

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Roorkee, Uttarakhand, India-247667

[email protected], [email protected]

Abstract

The fact that system reliability is influenced by numerous factors (model, assembling,

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installation, upkeep and use) makes it particularly challenging to identify, evaluate and anticipate the failure causes of system. To this effect, the current research work seeks to propose a quantitative and qualitative approach based integrated framework for the behaviour analysis of a

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process plant system. In the quantitative analysis, series/parallel combination of the considered

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system has been modeled using Petri net (PN) approach. Various reliability parameters were computed at different spreads and the system’s behaviour is studied in crisp, fuzzy and defuzzified terms. Further, in order to improve system’s availability and maintainability characteristics, an extensive qualitative analysis is performed using Root Cause Analysis (RCA) and Failure Mode and Effect Analysis (FMEA) for listing the various failure causes. Limitations of traditional FMEA in risk ranking of risky/critical components were overcome by using a Fuzzy Decision Making System (FDMS) and Grey Relation Analysis (GRA). The ranking

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results so obtained were compared with those of FMEA raking results for better decision making of risky components of the considered system. The framework has been employed to carry out the behavioural analysis of a real Water Treatment Plant (WTP) of a coal fired thermal power

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plant in North India.

Key words: Thermal power plant, Petri net, FMEA, Fuzzy Decision Making System, system

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availability. 1. Introduction

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Water plays an important role in the functioning of a coal fired thermal power plant, as it is required for various applications such as steam generation, ash disposal, condenser cooling, service water and potable water, among

and many others. Raw water taken from natural

resources like rivers and canals is first treated before using it for specific applications. Therefore, to ensure a continuous supply of treated water to the plant, it is necessary that the system should

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be available for long duration. For measuring the performance of the repairable industrial system availability plays an important role (Cochran, Murugan, & Krishnamurthy, 2000; Juang, Lin, & Kao, 2008).To this effect it is necessary to have suitable knowledge of behaviour of the

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system/subsystem which would help the system analyst in designing a suitable maintenance

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policy for the considered system (Sharma & Sharma, 2012). In an industrial system, failure is an inevitable phenomenon and can be minimized only by adopting a planned maintenance policy. However, there are certain issues such as inadequate inspection or testing, poor maintenance, human error, rapid technology advancement, inadequate and vague availability of failure/repair data that need to be addressed for analyzing the complex behaviour of the industrial system. Further, with advancement in technology and an increase in complexities of subsystems and equipment of a system, it becomes difficult for the system analyst to analyze the system’s 2

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behaviour using various qualitative and quantitative approaches (Adamyan & David, 2004; Aksu & Osman, 2006; Hauptmanns, 2011; Hu, Si, & Yang, 2010; Cai, 1996; Modarres & Kaminski, 1999; Vallem & Saravannan, 2011).

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2. Literature review

In the past, a number of researchers have carried out performance analysis of various operating systems belonging to different process industries such as urea plant, paper plant, sugar mill and

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thermal power plant. For instance, Arora and Kumar (1997, 2000) computed the availability of steam & power generating and ash handling units of a thermal power plant using the Markovian

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probabilistic approach. Gupta et al. (2005, 2007) applied the Markovian approach to compute reliability parameters (reliability, availability and MTBF) for butter oil and plastic-pipe manufacturing plants. The Markovian approach so used for performance analysis of systems requires large amount of data which are difficult to obtain due to human error, economic

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constraints, and complications involved in determining rare events of component failure, etc. Even if data are available from sources such as historical records, they are vague, contradictory or incomplete and results obtained through analyses of that data are highly uncertain. Therefore,

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predicting accurate behaviour or performance of the system becomes quite difficult for the system analyst. To overcome the limitations posed by incorrect data and subsequent uncertainty

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in results, approximations and estimates of probabilities provided by maintenance experts/maintenance managers (approximate reasoning) have been taken into consideration by different researchers. Although these estimates provided by maintenance experts are based on individual assumptions and calculations, yet they are useful in the assessment of system reliability when converted into fuzzy reasoning (approximate reasoning expressed in mathematical terms). To illustrate, Sergaki and Kalaitzakis (2002) developed a method built on fuzzy reasoning for maintenance planning in a thermal power plant. Liu et al. (2005) proposed a 3

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structured framework on the basis of rule based inference methodology for modeling and evaluating system safety of engineering systems which was later applied to an offshore and marine engineering system.

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The above mentioned studies show that approximate reasoning (AR) once converted into a more logical and consistent data form (fuzzy reasoning) and developed into an effective methodology (fuzzy methodology or FM) helps in dealing with uncertain, subjective and imprecise

information,

and

has

increasingly

been

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information more effectively and consistently. FM greatly eliminates vagueness in collected considered

as

an

effective

tool

for

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behavioural/performance analysis of various industrial systems by researchers. Knezevic and Odoom (2001) developed a quantitative (λ-τ) approach which uses fuzzy set theory instead of crisp set theory to account for uncertainty and compute various reliability parameters at different spreads for the reliability analysis of industrial system. Sharma et al. (2007) presented the

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modeling of system behaviour for risk and reliability analysis using KBARM. Two important unit namely forming and press unit of the paper plant has been considered for the analysis. Recently, Panchal and Kumar (2015) applied FM for studying the failure behaviour of Power

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generating unit of a thermal power plant in India. Garg et al. (2013) demonstrate the application of an artificial bee colony based Lambda–Tau (ABCBLT) methodology for predicting the

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behaviour of press unit in a paper industry. Garg and Sharma (2012) purposed a new fuzzy lambda tau approach for analyzing the behaviour of Synthesis unit of a urea plant. Panchal and Kumar (2014) expounded the application of FM based lambda tau methodology for the reliability analysis of Compressor house unit of a coal fired thermal power plant located in northern India. Guimara˜es and Lapa (2007) developed a fuzzy decision support system for criticality rating of risky components of a nuclear power plant. Sharma et al. (2005) developed a

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systematic fuzzy decision support system for prioritizing the critical components of a paper plant subsystem. Kumru and Kumru (2013) applied fuzzy FMEA approach for improving the purchase process in a public hospital and the problem which arisen from the conventional FMEA has been

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solved. Sharma et al. (2008) developed a knowledge based fuzzy decision support system using fuzzy FMEA approach for prioritizing the risky components of paper machine in a paper mill. Silva et al. (2014) proposed a FMEA and fuzzy theory based model for prioritizing the aspects of

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information security risk. The proposed multidimensional model has been applied to a university research group project where communication security was found as the important aspects of

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information security risk. Geum et al. (2011) developed a service specific FMEA and GRA based systematic approach for identifying and evaluating the potential failures. Biondini et al. (2004) used fuzzy logic for fuzzy reliability analysis of a concrete structure. Mustapha et al. (2004) demonstrated the use of FM in developing a computer based intelligent system for fault

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diagnosis of an aircraft engine. Konstandinidou et al. (2006) exhibited the application of fuzzy modeling for human reliability analysis. Liang and Weng (2002) used the fuzzy approach for evaluating quality improvement alternatives based on quality costs.

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The authors, on the basis of the literature reviewed, observed that the proposed integrated framework has not yet been used for the behaviour analysis of WTP of a coal fired thermal

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power plant. This paper seeks to fill this gap by applying the proposed framework comprising quantitative and qualitative approaches for the behavioural analysis of WTP in a coal fired thermal power plant situated in the northern part of India. 3. Proposed integrated framework The proposed integrated framework for the behaviour analysis of the considered system has following three phases (See fig. 1).

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Firstly, data/information is collected from the various sources such as maintenance log book and maintenance expert. Secondly, based on the collected failure rate and repair time data, reliability analysis of the considered system has been done using fuzzy λ- τ approach and the various

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reliability parameters such as reliability, availability, repair time, expected number of failure (ENOF) and Mean time between failure (MTBF) of the considered system were computed at different spread (± 15%, ± 25% and ± 60%) and the system failure behaviour is studied

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quantitative. Lastly, for improving the availability characteristics and to raise the maintainability requirement of the considered system, in-depth risk analyses have been done using Root Cause

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Analysis (RCA) and Failure Mode Effect Analysis (FMEA). The complete categorization of failure causes using RCA approach help to set up a knowledge base (in-depth information related to possible failure mode, functions of different component and their symptoms of failure) necessary for conducting FMEA. Limitations of traditional FMEA in risk ranking (based on RPN

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score) were overcome by using Fuzzy Decision Making System (FDMS) and Grey Relation Analysis (GRA) as both these approaches are useful in integrating the judgments of expert, experience and expertise in more flexible and pragmatic manner by using well-defined fuzzy

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membership functions related to values of probability of occurrence of failure (Of), severity(s),

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probability of non-detection (Od) and FMEA output for risk priority. < Insert fig 1 here>

The structure of this paper is as follows: Section 1 presents the introduction. Literature review is discussed in section 2. Section 3 discusses the proposed integrated framework. Section 4 presents the various failure analysis methods. Section 5 discusses about the basic definition of fuzzy logic. Section 6 discusses about the grey relation approach. Section 7 demonstrates the application of proposed integrated framework for behaviour analysis of the considered system.

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Finally, Section 8 and 9 presents the conclusions and managerial implications of the research work. 4. Failure analysis method

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This section discusses the various method used in the present study for the failure analysis of the considered system. 4.1 Root Cause Analysis

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In order to minimize the future failure of the system RCA plays an important role. In the present study, various failure causes related to each subsystem of the considered system were listed on

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the basis of field expert and the information so collected is helpful for establishing a knowledge base for conducting its Failure Mode Effect Analysis (FMEA). 4.2 FMEA

FMEA is a systematic approach, used to identify possible failure causes in functional designs of

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the considered systems/subsystems, and then determine the frequency and impact of the failure on those systems/subsystems, and develop apposite preventive measures to eliminate or minimize the risk of potential failures for improving system availability (Bowles, 2003; Ebeling,

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2001; O’Connor, 2000; Sharma et al., 2005b; Tay and Lim, 2006; Xu et al., 2002). In the present

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study, for the risk analysis of the considered system, FMEA approach has been applied and then its limitations (Sharma et al., 2005b) in risk ranking were overcomed by applying FDMS and GRA approach.

4.3 FTA and PN theory

FTA and PN are reliability tools which make use of AND/OR symbols for representing the series/parallel combinations of various sub-systems of the considered system where PN is considered a better approach as compared to FTA, as it is easier to obtain minimal cut set and

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path set (Petri, 1962; Adamyan & He, 2004; Liu & Chiou, 1997; O’Connor, 2000; Singh & Dhillion, 1989). The general representation of the PN model corresponding to the AND/OR gate used in the present study is shown in fig 2.

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< Insert fig 2 here> 5. Basic fuzzy logic definitions

The basic notions of fuzzy set theory used in present study (Kokso, 1999; Ross, 2000; Tanaka,

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2001; Zadeh, 1996; Zimmermann, 1996) are defined as:

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5.1 Crisp set and fuzzy set:

Mathematically, the crisp set and fuzzy set is defined by equation 1 and 2 and is represented as: 1,      =  0,   ∉ 

  :  → 0,1

(1) (2)

Where, → universe of discourse,  → element of ,  → crisp set, → characteristic

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function/ indicator function and  → degree of membership for element  in fuzzy set . 5.2 Membership function (MF) and interval analysis:

A TMF μ   of a fuzzy set  = , ,   in universe  is defined by equation: '#"

'#%

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"#$

,  ≤  ≤ 

(4)

 0 , ()ℎ+,-.+

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µ    =

! %#$ ,  ≤  ≤ 

Where, , , → the upper, mean and lower bound respectively.

Also, the confidence interval as defined by α-cut for a TFN is given by equation 5 and is represented in fig 3.

/ 0 = 1 − 0 3 + 0 , −5 0 − 63 +  0 7  < Insert fig 3 here> 8

(5)

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5.3 Linguistic variables and fuzzy rule base: For vaguely defined event, the linguistic variables ("very low"," low", "high", "very high", etc.) are used to express the subjective judgment of experts in a quantitative form on the basis of a

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well-defined probability scale. In fuzzy rule base the format of IF -THEN rules is represented by equation 6.

8 :   . 9: )ℎ+; < . =: -ℎ+,+  = 1,2,3 … … … . . B

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Where,

(6)

 → antecedent which is compared to the input; )ℎ+;→ consequent, which is the result; →

→consequent linguistic constant.

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input linguistic variable; 9: →antecedent linguistic constant; <→ output linguistic variable; =: 5.4 Fuzzy inference system and defuzzification:

Fuzzy inference system uses fuzzified input and IF-THEN rules for giving the fuzzified output.

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Due to high accuracy in the defuzzification results (lie approximately in the middle of the area), Center of area (COA) method has been applied by the authors in the present study.

C =

H J H "G" DH I µ E J F

DH I ".µ E F "G"

Where,

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Mathematically, COA method is defined as:

(7)

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K → output fuzzy set; µ   → membership function (MF).

6. Grey relation approach (GRA)

Grey relation approach (Deng, 1986) is an effective decision making approach used for exploring the system’s behaviour using relation analysis and model construction (Liu & Lin, 1998; Wu &

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represented by equations (Deng, 1989; Pramanik & Mukhopadhyaya, 2011). L: = 5<: 1, <: 2, … . <: N  … … <: ;6,

(8)

Oℎ+,+  = 1,2, … . . , P and Q = 1,2, … . . , ; (9)

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LM = 5

respectively. The grey relation coefficient R5
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R5
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relation as follows (Deng, 1989; Pramanik & Mukhopadhyaya, 2011).

(10)

Where, ζ ∈ 0,1 is an identifier; assumed to be 0.5.

Then the degree of relation for each failure cause is computed by using equation Γ = β8 γ8^ + β_ γ_^ + β` γ`^ , -)ℎ ∑TUc8 βb = 1

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Where, βb → weighting coefficient of the decision factors

(11)

In the present study, βb is computed on the basis of expert feedback using Wang scale shown in

7. An application

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table 1 (Wang et al., 2007).

< Insert table 1 here>

To illustrate the application of FM, a WTP system of a coal fired thermal power plant has been

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considered in this study. This WTP system consists of   Clariflouculator,   Classifier water

pump,   Sent pressure filter, d  A.C (activated carbon) filters and d  condensate storage

tank.

Alum-chlorine treated water is collected in the classifier where dust and sand particles settle down in slug form and pure water is delivered to the DM plant through classifier service water

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pump. It then passes through the filters and anion-cation tank for further cleaning and is collected in a condensate storage tank for plant use. The schematic diagram of WTP is shown in fig 4.

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  Clariflouculator [CF1]: It consists of two units in parallel configuration used to supply pure water to the plant and the colony. The failure of one unit reduces the capacity of the system.

  Classifier water pump [CW2]: It consists of three pumps in parallel configuration used to

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deliver the water to raw water sump or DM plant. The failure of a pump reduces the capacity to supply water.

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 Sent pressure filter [SP3]: It consists of four sent pressure filters, two of which are

functional while the other two are in standby.

d  A.C filters [AF4]: It consists of three filters (two functional and one reserve) used to further filter water.

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d Condensate storage tank [CT4]: Arranged in series with the system and used to collect purified water.

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< Insert fig 4 here>

7.1 Reliability analysis

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To evaluate reliability of the considered system, the PN model was developed from a equivalent FT diagram as shown in fig 5(a) and 5(b). The procedural steps involved in fuzzy λ-τ approach

are   Information extraction from various sources   Computation of fuzzy numbers  

Reliability parameters estimation d Defuzzification.

< Insert fig 5(a) here> < Insert fig 5(b) here>

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7.1.1 Information extraction from various sources: Under this step, the failure/repair time data in the form of λ-τ for each component/subsystem of WTP was extracted from the plant expert as shown in table 2 (Kaushik & Singh, 1992). < Insert table 2 here>

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maintenance log book and is verified with the suggestion and opinion of maintenance field

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7.1.2 Computation of fuzzy numbers: The data obtained from various sources was imprecise and the results obtained using the data might also contain an element of uncertainty. To deal with uncertainties in collected failure /repair time data, it was converted into TFN (at ± 15%, ± 25%,

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and ± 60% spread) using TMF. Fig 6 shows the TFN at ± 15% for  = 1gh component of the

WTP. Further, using the extension principle, coupled alpha cuts and interval arithmetic operations on basic λ-τ expressions (table 3) for AND/OR gates (Singh & Dhillion, 1989), the

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resultant fuzzy numbers for failure/repair time for the top place of PN model were computed. The interval expressions used for fuzzy numbers with TMF for AND/OR gate transition expressions are represented by equations 12-14.

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< Insert fig 6 here>

< Insert table 3 here>

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Similarly, the fuzzy AND /OR gate expression for failure and repair rate can be represented as follows

AND gate transition expression i0 = j∏T:c8li:_ − i:8 3 + i:8 m. ∑Tpc8 j∏T:c8ln:_ − n:8 3m + n:8 q , :op

∏T:c8l−i:` − i:_ 3 + i:` m. ∑Tpc8 j∏T:c8ln:` − n:_ 3m + n:` q :op

12

(12)

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∏t ZuJlsZI #sZJ 0[sZJ m

t ∑t yuJv∏ZuJl#sZw #sZI 0m[sZw z Zxy

,

∏t ZuJl{Zw #{ZI 0[{Zw m

t ∑t yuZv∏ZuJlsZI #sZJ 0m[sZJ z Zxy

|

(13)

OR gate transition expression i0 = ∑T:c8li:_ − i:8 3 + i:8 m, ∑T:c8l−i:` − i:_ 3 + i:` m ∑t ZuJl#{Zw #{ZI 0[{Zw m

∑t ZuJl#{Zw #{ZI 0[{Zw m.l#sZw #sZI 0[sZw m ∑t ZuJl{ZI #{ZJ 0[{ZJ m

,

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∑t ZuJl{ZI #{ZJ 0[{ZJ m.lsZI #sZJ 0[sZJ m

(14)

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n0 =

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n0 = r

7.1.3 Reliability parameters estimation: Once the collected failure/repair time crisp data is fuzzified, different reliability parameters using various reliability expressions as shown in table 4 were computed for right and left side spreads at ± 15%, ± 25% and ± 60% spreads for different

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alpha cuts. The alpha cut range lies within 0-1, with an increment of 0.1. < Insert table 4 here>

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Table 5 and table 6 represent the left and right side spread fuzzy values at ± 15% spread for the various reliability parameters of the WTP system. Fig. 7 (a-d) shows the graphs for fuzzy values

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(± 15, ± 25% and ± 60% spreads) at different alpha cuts for various reliability parameters. < Insert table 5 here> < Insert table 6 here> < Insert fig 7(a-d) here> 7.1.4 Defuzzification: Under this step, for accurate behaviour study of the considered system the fuzzy values of different reliability parameters obtained at different spreads were converted in 13

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crisp form as shown in table 7, and the system’s behaviour is studied for different reliability parameters (at ±15%, ±25% and ±60% spread) with graphs as shown in fig 8.

< Insert table 8 here> < Insert fig 8 here>

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7.1.4.1 Behavioral analysis

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< Insert table 7 here>

Table 7 represents the crisp and defuzzified values at different spreads (±15%, ±25% and ±60%)

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where the defuzzified values changed with a change in spread while the crisp values remained constant. Table 8 indicates that defuzzified values for the failure rate first increased to 0.00137527% with an increase in spread from ± 15% to ±25% and increased further to 0.00120192% with a change in spread from ± 25% to ±60%. A similar pattern was observed for

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other reliability parameters such as repair time, unavailability, unreliability, MTBF and ENOF, which also increased with an increase in spread. On the other hand, system reliability first decreased to 0.00000715% with an increase in spread from ± 15% to ±25% and decreased

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further to 0.00078055% when spread changed from ± 25% to ± 60%. The defuzzified values show increasing and decreasing trends thus proving that the values obtained through FM are

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conservative in nature. The trends mentioned above are of great importance for maintenance personnel/system analysts. It is on the basis of these trends that system analysts study the system’s behavior and make decisions regarding system maintenance. Table 8 also shows that repair time changes more rapidly than any other reliability parameter. Thus maintenance decisions should be based on defuzzified values rather than crisp values. Based on the observations, maintenance personnel/system analysts may choose a feasible defuzzified value

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rather than a crisp value in order to achieve the targeted goal of maximum profit. Since, the availability of the considered system goes on decreasing with increase in its repair time therefore, to improve the availability characteristics and to enhance the maintainability

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requirement of the considered system it is decided to conduct its in-depth risk analysis using RCA and FMEA.

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7.2 Risk analysis

To improve availability characteristics and to enhance the maintainability characteristics of the

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considered system, the author has applied RCA and FMEA approaches in this study. In RCA various causes of system unreliability are listed, and an RCA diagram showing the possible failure causes associated with the various sub-systems namely, Clariflouculator, Filters, Classifier pumps and condensate storage tank is given in fig 9.

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< Insert fig 9 here>

On the basis of data collected from sources such as the plant maintenance log book, and expert opinion, FMEA analysis has been done using a well-defined linguistic assessment scale as shown

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in table 9 (generated on the basis of maintenance expert feedback). The scores related to probability of occurrence of failure (Of), severity of failure (S) and probability of non-detection

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(Od) (Sharma et al., 2005b), of various components of the system were computed and used to calculate RPN values (RPN=Of ×S× Od) as shown in table 10. < Insert table 9 here> < Insert table 10 here>

It may be observed from table 10 that: the causes with different set of linguistic terms may have same RPN, and the causes with same set of linguistic terms may have different RPN.

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linguistic terms (High, High, Moderate) but have different RPN (196 and 245) score and ranked

as 3rd and 1st (table 14) respectively. ) The causes CP1 and CP6 of classifier pumps are

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represented by different set of linguistic terms (High, Moderate, Moderate and Moderate, High, Moderate) but have same RPN (288) values and same rank is given to these causes which could be misleading as far as the system analyst is concerned. These types of limitations associated

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with FMEA in priority allocation in FMEA are addressed by using FDMS and GRA. 7.2.1 FDMS application

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A FDMS was developed using Fuzzy Logic MAT LAB (R2012a) Toolbox, having three modules namely knowledge base module, input inference module and output inference module (Fig 10).

< Insert fig 10 here>

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In FDMS the computed values of Of, S, Od in table 10 were considered as input for FRPN output which were first fuzzified using trapezoidal membership function as shown in fig 11. < Insert fig 11 here>

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In the function, five linguistic terms: very low, low, moderate, high and very high were used to

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describe the three input variables (Klir & Yuan, 1995) thus forming five fuzzy sets, and generating 125 rules which were reduced to 30 by combining the rules. Fig. 12 shows the set of IF-THEN rules used in the study. < Insert fig 12 here>

Fuzzy output was obtained by applying the IF-THEN rules in fuzzy inference engine along with fuzzified inputs, and using TMF (fig. 13).

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< Insert fig 13 here> For defuzzification of fuzzified outputs, the Centroid method was used and the crisp value so

< Insert fig 14 here> 7.2.2 GRA application

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obtained for risk ranking is given in table 14. Fig 14 shows the FRPN output for cause CS1.

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Grey relation approach has been applied within FDMS for prioritizing the various failure causes listed in FMEA sheet (table 10). Further, using Chen and Klien's method (Chen& Klien, 1997),

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defuzzification of five linguistic terms (same as defined for FDMS) has been done to obtain their crisp number and shown in table 11 with their defined symbols. < Insert table 11 here>

The crisp values so obtained for different linguistic terms were used to generate the comparison

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series. For example, matrix equation 15 represents the comparison series for Clariflouculator failure causes listed in table 10.

0.9125 0.7272 0.9125 0.7272

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+ +++ + 0.5118 + + + + + }+ + + + + +~ = }0.5118 0.5118 + ++ + 0.5118

0.5118 0.7272~ 0.7272 0.5118

(15)

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Where left side of the equation 15 represents the set of linguistic terms for failure causes with their defined symbols and right side represents corresponding crisp numbers. Similarly, comparative series were generated for other subsystems associated with the considered system. Then using the lowest possible level of linguistic term defuzzified value (for very low crisp number is 0.0937, which represents the average value 0), a standard series (equation 16) for the Clariflouculator has been developed. Similarly, standard series are formed for other subsystems.

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× × × ×

0 × ×~ = }0 × 0 × 0

0 0 0 0

0 0~ 0 0

(16)

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Further, for obtaining the grey relation coefficient using equation 10, the difference between comparison and standard series is calculated which is same as comparative series (equation 15). Similarly, the difference for other subsystems has been computed. Using these values of grey

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relation coefficient and introducing the weighting coefficients βb  , for all linguistic variables,

the degree of coefficient or grey output is computed for each failure cause and the ranking is

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done accordingly. Here, for computing the weighting coefficient βb  for the linguistic variables, Of, S and Od , a comparison matrix has been prepared (on the basis of expert feedback) and their

weights were computed using fuzzy extent analysis method (Chang, 1992; Chan, 2008) as shown in table 12 (Kutlu & Ekmekçioğlu, 2012; Wang et al., 2007).

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< Insert table 12 here>

Using these weights for the risk factors, the degree of coefficient and grey output is computed by using equation 11. Similarly, the grey output values for other failure causes have been computed

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as shown in table 13 and the comparison of ranking results obtained on the basis of FMEA, FDMS and GRA are shown jointly in table 14 which are useful for the system analyst for

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allocation of risk priorities.

< Insert table 13 here> < Insert table 14 here >

7.2.3 Discussion Table 14 compares the ranking results given by RPN, FRPN and Grey analysis. It is observed that using traditional FMEA, causes CS1 and CS3 of the condensate tank represented by the same

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set of linguistic terms (High, High, Moderate) produced different RPN values (196 and 245 respectively), which suggests that CS3 and CS1 should be ranked 1st and 3rd respectively, but this could be misleading; fuzzy and Grey approaches gave defuzzified output of 7.37 and Grey

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relation output of 0.4895 for both CS1 and CS3, thus, both causes were ranked 1st. This entails giving same priority for attention to both causes. Similarly, causes CP1 and CP6 for classifier pumps are represented by different linguistic terms (High, Moderate, Moderate and Moderate,

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High, Moderate) and produce same RPN values (288) using traditional FMEA. This suggests that both causes should be ranked 1st, but FRPN and Grey outputs are different and priorities are

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assigned accordingly. Here, due to the weighting coefficient (arrived at by incorporating expert opinion and performing AHP analysis) and a high severity effect, CP6 is given a higher priority than CP1. The above discussion makes it clear that comparison results (Table 14) are more helpful to decision makers in deciding the risk priorities for various subsystems of a given

FRPN and grey output. 8. Conclusion

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system as for most of the failure causes similar ranking results are obtained on the basis of

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The present study focuses on the application of integrated framework for behaviour analysis of the WTP system which helps the system analyst in predicting the behaviour for the system. The

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system analyst/maintenance manager’s decision is based on imprecise information collected from various sources such as maintenance log book and the opinions of the maintenance expert etc. Owing to its sound logic in the quantification of imprecise information for the considered system, it not only reduces the uncertainty in results but also helps system analysts in better decision making. For the reliability analysis, with vague information various reliability indices such as failure rate, repair rate, reliability, MTBF, ENOF and availability are computed at

19

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different spreads and the failure dynamics has been studied. Failure causes contributing to system unreliability are tabulated in table 10, RPN, FRPN and grey outputs were computed to prioritize the various failure causes and their risk priorities are allocated accordingly. The

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comparison ranking results so obtained using RPN, FRPN and grey output are helpful for the system analyst to arrive at better decisions regarding risky components. 9. Managerial implications

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The key managerial implications derived from the present research work are that the proposed integrated framework, which make use of qualitative and quantitative approaches for the

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behavioural analysis of WTP in coal fired thermal power plant, will help the system analyst in following ways:

To help the system analyst predict the system’s behaviour under uncertainty.

•

To identify the risky components of the system using subjective judgments of experts.

•

To help system analyst in designing the optimal maintenance policy for the considered system.

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•

The results obtained for the risk analysis are based on the expert's judgment (for developing a

EP

knowledge base of fuzzy inference system) and the quality of information obtained from the various sources. Therefore, for high accuracy of the results one should take care of to ensure high

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accuracy in the input data without which the results could be biased. However, the findings of this research work have been discussed with the maintenance manager of the considered system and with the outcomes of this work the maintenance manager seems to shows an agreement. Once the top management of the considered system decides to implement the finding of the current research work for improving the system availability, a detailed verification and validation of the proposed integrated framework can be conducted accordingly.

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Acknowledgement The authors are thankful to Haryana power generation corporation Ltd. (HPGCL) Haryana, India for supporting this work. The authors are also thankful to Indian Institute of Technology-

…G

Nomenclature Membership Function

TFN

Triangular Fuzzy Number

TMF

Triangular Membership Function

FTA

Fault tree analysis

PN

Petri Net

FM

Fuzzy Methodology

FMF

Fuzzy membership function

MTBF

Mean Time Between Failure

RCA

Root Cause Analysis

FMEA

Failure Mode Effect Analysis

RPN

Risk Priority Number

FRPN

Fuzzy Risk Priority Number

FDMS

Fuzzy Decision Making System

GRA

Grey Relation Analysis

AHP

Analytical Hierarchy Structure

.

…† i:

Probability of non detection Severity of failure

Probability of occurrence of failure Greek symbols

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MF

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Annexure:

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Roorkee for providing the facility for conducting this research work.

n:

i0

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EP

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n0

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βb

Failure rate of components

Repair time of components Interval for fuzzy failure rate Interval for fuzzy repair time Weighting coefficient

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Publishers.

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Table 1: Fuzzy judgment linguistic scale (Wang et al., 2007) fuzzy scale 1/2,1,2 (X-1,X,X+1) (1/X+1,1/X,1/X-1) (Y, Y+Z/2,Z) (1/Z,2/Y+Z,1/y)

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Uncertain judgment Approximately important Approximately X time more important a Approximately X time less important Between Y and Z time more important b Between Y and Z time less important

Table 2: Failure rate/repair time data subsystems

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(aX =2,3,………..9 ),( b Y, z =1,2,…......9, Y< Z)

Failure rate (λi) (Failures/hr)

Potable water pump ( = 1,2)

1.15 × 10

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Classifier pump ( = 3,4,5)

Sent pressure filter ( = 6,7,8,9)

A.C filter ( = 10,11,12)

3.858 × 10  3.858 × 10  5.787 × 10 

8 8 12 12 10

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EP

CST tank ( = 13)

1.15 × 10 

Repair time (τi) (hrs)

Table 3: Basic Expression for OR/AND gate gate

n-Input gate expression







∑!"  $ ∑!" 

!"



 % &'∑+ &!"

+ .,- ∏*,-,.* )* /

 ∏!" $

∑ 0∏ &!" !",& $ 1

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Table 4: Various reliability parameter Reliability indices

Expression

Mean time to failure

23345 =

"

67 " µ7

23945 = 23345 + 23385

;5 =

µ 7 <67 67 @

85 = =

ABC4 =

+

67

µ 7 <67

67 µ 7 @

µ 7 <67

+

= >µ 7 <67 ?@ 67 D

D

>µ 7 <67 ?

01 − = >µ 7 <67 ?@ 1

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EP

TE D

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Reliability Expected number of failures

µ7

SC

Mean time between failure Availability

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23385 =

Mean time to repair

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Table 5: Left side spread at 15% for different reliability parameters

Availability 0.999421 0.999385 0.999347 0.999307 0.999265 0.999221 0.999174 0.999125 0.999073 0.999018 0.998961

Unavailability 0.000579 0.000545 0.000513 0.000482 0.000453 0.000426 0.000400 0.000375 0.000352 0.000330 0.000309

Reliability 0.990289 0.990143 0.989997 0.989851 0.989705 0.989559 0.989413 0.989266 0.989120 0.988974 0.988828

Unreliability 0.009711 0.009565 0.009419 0.009273 0.009127 0.008981 0.008835 0.008689 0.008543 0.008398 0.008252

MTBF 17226.3569 16969.5948 16720.2884 16478.1167 16242.7767 16013.9825 15791.4638 15574.9652 15364.2447 15159.0733 14959.2338

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Repair time 9.977884 9.535838 9.111165 8.703109 8.310959 7.934044 7.571728 7.223413 6.888531 6.566545 6.256947

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Failure rate 0.000058 0.000057 0.000056 0.000055 0.000055 0.000054 0.000053 0.000052 0.000051 0.000050 0.000049

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Dof 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

ENOF 0.009753 0.009606 0.009459 0.009312 0.009165 0.009018 0.008871 0.008724 0.008577 0.008430 0.008283

Table 6: Right side spread at 15% for different reliability parameters

Unavailability 0.000579 0.000615 0.000653 0.000693 0.000735 0.000779 0.000826 0.000875 0.000927 0.000982 0.001039

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Availability 0.999421 0.999455 0.999487 0.999518 0.999547 0.999574 0.999600 0.999625 0.999648 0.999670 0.999691

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1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Repair time 9.977884 10.438104 10.917349 11.416525 11.936596 12.478590 13.043601 13.632800 14.247439 14.888855 15.558483

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Dof

Failure rate 0.000058 0.000059 0.000060 0.000061 0.000062 0.000062 0.000063 0.000064 0.000065 0.000066 0.000067

Reliability 0.990289 0.990435 0.990581 0.990727 0.990873 0.991019 0.991165 0.991311 0.991457 0.991602 0.991748

Unreliability 0.009711 0.009857 0.010003 0.010149 0.010295 0.010441 0.010587 0.010734 0.010880 0.011026 0.011172

MTBF 17226.3569 17490.9153 17763.6316 18044.8903 18335.1002 18634.6969 18944.1446 19263.9384 19594.6075 19936.7176 20290.8745

ENOF 0.009753 0.009900 0.010047 0.010194 0.010341 0.010488 0.010636 0.010783 0.010930 0.011077 0.011225

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Table 7: Crisp and defuzzified value at different spread

Failure rate (h-1) Repair time (h) Availability Unavailability Reliability Unreliability MTBF ENOF

0.00005808 9.97788379 0.99942078 0.00057922 0.99028931 0.00971069 17226.3569 0.00975283

Defuzzified value(±15% spread) 0.00005809 10.5997771 0.99935763 0.00064237 0.99028842 0.00971158 17492.1551 0.00975364

Defuzzified value(±25% spread)

Defuzzified value(±60% spread)

0.00005817 16.8489592 0.99871683 0.00128317 0.99028134 0.00971866 20158.4808 0.00975894

0.00005824 25.3009706 0.99793728 0.00206272 0.99027515 0.00972485 23721.8867 0.00976192

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Crisp value

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System parameters

Table 8: Change in defuzzified value for spread change in %

± 15% to ±25% 0.00137527 0.37089425 0.49938823 0.00072849 0.13226818 0.00054309 0.00000715 0.00064121

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± 0% to ±15% 0.00017215 0.05867041 0.09830782 0.00009164 0.01519528 0.00008305 0.00000089 0.00006318

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Failure rate Repair time Unavailability Unreliability MTBF ENOF Reliability Availability

Change in defuzzified value in %

EP

Reliability parameters

± 25% to ±60% 0.00120192 0.33405878 0.37792332 0.00063651 0.15021596 0.00030527 0.00000625 0.00078055

Trend Increasing Increasing Increasing Increasing Increasing Increasing Decreasing Decreasing

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Table 9: Scale used for probability of failure occurrence, severity and probability of nondetection

Very low

1

>5 years

<0.01

Low

2/3

2-5 years

0.01-0.1

Moderate

4/5/6

1-2 years

0.1-0.5

High

7/8

0.5-1year

0.5-1

Very high

9/10

< 6 months

>1

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Severity effect

Likelihood of non-detection (%)

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Occurrence rate (%)

Not noticed

0-5

minor infuriation to operator minor fall in system performance Considerable deterioration in system performance Power generation loss

6-15/16-25

SC

MTBF

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Score/rank No.

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Linguistic terms

26-35/36-45/ 46-55 56-65/66-75

76-85/86-100

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Table 10: FMEA sheet for WTP based on experts opinion Function

Potential mode

disposal To dispose the slug

Fails to properly

failure Potential effect of Potential cause of failure failure

Of

S

Od

6

9

4

216

Internal broken[CF2]

5

8

8

320

Mechanical failure[CF3]

4

9

7

252

Operation loss

Clogging[CF4]

6

7

5

210

Loss in pressure

Corrosion[SP1]

6

7

6

252

Abrasion[SP2]

7

8

5

280

Operational efficiency Dust particle[SP3] loss

5

5

5

125

Loss of flow

Seal cutting or seal failure [CP1]

8

6

6

288

Operational efficiency Foreign particles presence [CP2] loss

6

7

3

126

Scanty lubrication in moving 4 parts[CP3]

7

5

140

Inclusion of solid particles [CP4]

4

7

4

112

Overheating [CP5]

6

6

6

216

Improper lubrication[CP6]

6

8

6

288

Worn out bearing [CP7]

5

7

3

105

Clariflouculator

To collect the slug

Fails open Fail close

Sent pressure and To filter the water A.C filters

Wear/tear

Classifier pump

EP

Suction and To allow water flow to leakage discharge valve enter the pump and to flow from the pump

Bearing

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Chocking

Impeller

Fail to collect slug

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Valve chocking

slug Faulty valve operator[CF1]

SC

Flappers

open Let down disposal

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Slug valve

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Components

To increase the pressure Jamming and flow of water To reduce friction on Bearing seizure rotating shaft

Vibration

Operational loss

Operational loss

Pump efficiency loss

RPN

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Cavitations [CP8] To supply power for Winding insulation Complete pump operation deteriorate loss

Condensate storage tank

To collect pure water

Chocking

Loss of pure water

90

6

9

3

162

Wear/tear[CS1]

7

7

4

196

Corrosion[CS2]

5

7

6

210

7

7

5

245

Improper tank cleaning[CS4]

3

7

6

168

Blockage [CS5]

5

6

6

180

Operational efficiency Scale formation [CS3] loss

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6

operation Motor burn out [CP9]

SC

To spray pure water

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Spray nozzle

leakage

5

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Pump Motor

3

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Table 11: Defuzzified crisp number for linguistic terms Crisp number

Very low

×

0.0937

Low

××

Moderate

+

High

++

Very high

+++

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Symbol

0.2650 0.5118 0.7272 0.9125

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SC

Linguistic term

Table12: Comparison matrix and weights for risk factors S

Od

Weights

Of

(111)

(1/4,1/3,1/2)

(1/2,1,2)

0.20

S

(2,3,4)

(111)

(1,2,3)

0.56

Od

(1/2,1,2)

(1/3,1/2,1)

(111)

0.24

AC C

EP

TE D

Of

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Table13: Degree of coefficient and grey output results for various causes Of

HI

S

HJ

Od

HK

Grey output

CF1

+

0.5680

+++

0.4017

+

0.5680

0.4749

CF2

+

0.5680

++

0.4647

++

0.4647

0.4854

CF3

+

0.5680

+++

0.4017

++

0.4647

0.4501

CF4

+

0.5680

++

0.4647

+

0.5680

0.5102

SP1

+

0.5680

++

0.4647

+

0.5680

0.5102

SP2

++

0.4647

++

0.4647

+

0.5680

0.4895

SP3

+

0.5680

+

0.5680

+

0.5680

0.5680

CP1

++

0.4647

+

0.5680

CP2

+

0.5680

++

0.4647

CP3

+

0.5680

++

0.4647

CP4

+

0.5680

++

CP5

+

0.5680

CP6

+

CP7

SC

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Failure cause

0.5680

0.5473

××

0.7624

0.5568

+

0.5680

0.5102

0.4647

+

0.5680

0.5102

+

0.5680

+

0.5680

0.5680

0.5680

++

0.4647

+

0.5680

0.5102

+

0.5680

++

0.4647

××

0.7624

0.5568

CP8

××

0.7624

+

0.5680

+

0.5680

0.6069

CP9

+

0.5680

+++

0.4017

××

0.7624

0.5215

CS1

++

0.4647

++

0.4647

+

0.5680

0.4895

CS2

+

0.5680

++

0.4647

+

0.5680

0.5102

CS3

++

0.4647

++

0.4647

+

0.5680

0.4895

CS4

××

0.7624

++

0.4647

+

0.5680

0.5490

CS5

+

0.5680

+

0.5680

+

0.5680

0.5680

AC C

EP

TE D

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+

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Table 14: Comparison of traditional FMEA, FRPN and grey ranking results RPN output 216

RPN ranking 3

FRPN output 6.00

Fuzzy ranking 2

CF2

320

1

5.79

3

CF3

252

2

6.68

1

CF4

210

4

5.13

4

SP1

252

2

5.21

2

SP2

280

1

7.34

1

SP3

125

3

4.50

CP1

288

1

5.38

CP2

126

5

CP3

140

4

CP4

112

6

CP5

216

2

CP6

288

1

CP7

105

7

CP8

90

CP9

162

CS1

196

CS2

210

CS3

245

CS4 CS5

Gray output

Gray ranking

0.4749

2

RI PT

Failure codes CF1

3

0.4501

1

0.5102

4

0.5102

2

0.4895

1

3

0.5680

3

3

0.5473

3

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SC

0.4854

4

0.5568

4

4.75

7

0.5102

1

5.63

1

0.5102

1

5.21

5

0.5680

5

5.21

5

0.5102

1

5.00

6

0.5568

4

TE D

5.27

4.50

8

0.6069

6

3

5.47

2

0.5215

2

3

7.37

1

0.4895

1

2

4.75

3

0.5102

2

1

7.37

1

0.4895

1

168

5

6.00

2

0.5490

3

180

4

4.75

3

0.5680

4

AC C

EP

8

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Data collection sources

Maintenance expert opinion

Phase 1 Reliability analysis

Maintenance log book

SC

Compute reliability parameters using fuzzy λ-τ approach

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Phase 1 Data collection

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System Reliability Analysis

Root Cause Analysis Phase 2 Risk analysis

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Failure Mode Effect Analysis

Fuzzy Decision Making System

EP

Grey Relation Analysis

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Component risk ranking comparison System risk analysis

` Fig. 1 Integrated framework

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Arc T1

Place

T3

T2

T4

T3

T4

SC

T2

Transition

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T1

OR gate Petri net model

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AND gate Petri net model

EP

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Fig. 2 PN model for AND/OR gate

μ‫)ݔ(ܣ‬

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Ã

0

ܲ

ܲ(ߙ)

ܳ

ܳ(ߙ)

Fig. 3 α- cut of a fuzzy set

ܴ

SC

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Potable water for Colony Canal

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Sent pressure filter

Raw water sump

1

1

1

1

A.C filter

2

2

2

2

3

Clariflouculator

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3

3

4

Classifier service water pump

AC C

EP

Anion-cation tank

Mixed bed

Condensate storage tank

Fig. 4 Schematic diagram of WTP

A

C

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WTP

AND

2

SPF

AND

AND

3

4

5

AND

7

6

13

AC

8

9

10

11

12

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1

CP

SC

CF

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OR

WTP

AC C

EP

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Fig 5 (a): FTA model for WTP system

CF

1

C.P

2

3

4

5

6

7

13

AC

SF

8

9

Fig 5 (b): PN model for WTP

10

11

12

૚. ૚૞ × ૚૙ି૝

૚. ૙ૠ × ૚૙

ି૝

૚. ૜૜ × ૚૙ି૝ ି૝

0

૟. ૡ

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ૢ. ૡ૜ × ૚૙ି૞

૚. ૛૝ × ૚૙

EP

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Fig. 6 input fuzzy triangular number representation

AC C

0

μ‫)ݔ(ܣ‬

Ã

SC

μ‫)ݔ(ܣ‬

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ૠ. ૝

Ã

ૢ. ૛

8 ૡ. ૟

(b)

AC C

EP

TE D

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(a)

SC

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(c)

SC

(d)

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AC C

EP

TE D

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Fig.7 (a-d) Fuzzy representation of reliability parameters at ±15, ±25 and ± 60 % spreads

AC C

EP

TE D

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SC

RI PT

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SC

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AC C

EP

TE D

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Fig. 8 Fuzzy behaviour of system's parameters: (A) failure rate, (B) repair time, (C) availability, (D) unavailability, (E) reliability, (F) unreliability, (G) MTBF, (H) ENOF

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.

Clariflouculator

Filters

Valve operation problem Over slug

Scanty lubrication

Tank leakage

Pipe line chocking Valve chocking

Flappers malfunctioning

Strainer wipe out Pipe line chocking

Dust particle

SC

Corrosion

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Motor jamming

Water treatment plant

Vibration Spray nozzle blocking

Bearing jamming Over heating

Noisy operation

Spray nozzle chocking

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Scale formation Poor greasing

Seal cutting/failure

Classifier pumps

Condensate storage tank

EP

TE D

Fig.9 Root cause analysis of WTP

AC C

Foreign/wear particles

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Input membership function

RI PT

Knowledge base data analysis, Expert knowledge

Output membership function

SC

IF-THEN rules

Fuzzy inference engine

Defuzzification

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Fuzzification

Input Of, S, Od

Fuzzy input

FRPN Output

Fuzzy output

TE D

Fig. 10 Flow diagram of fuzzy FMEA approach

V. Low

Low

0

Moderate

High

V. High

EP AC C

Membership Value

1

1

2

3

4

5

6

7

8

Fig. 11 Trapezoidal membership function for input variable

9

10

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SC

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Fig.12 Format of IF-THEN rules

V. Low

Low

Moderate

Imp

H. Imp

V.H. Imp

0

2

AC C

1

EP

TE D

N. Imp

Membership Value

1

3

4

5

6

Fig. 13 TMF for output variable

7

8

9

10

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SC

RI PT

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AC C

EP

TE D

Fig.14 FRPN output for cause CS1

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•

RI PT

Highlights

An integrated framework has been proposed for the behaviour analysis and is illustrated with the help of Water Treatment Plant in a thermal power plant located in northern India. Petri net (PN) has been used for system modeling.

•

In the reliability analysis fuzzy Lambda-Tau approach has been used to compute the

M AN U

SC

•

various reliability parameters at different spread and the system behaviour is studied under uncertainty. •

In order to increase system reliability, an extensive qualitative analysis is performed using Root Cause Analysis (RCA) and Failure Mode and Effect Analysis (FMEA) for

TE D

listing the various failure causes.RPN scores are computed and ranking of risky component has been done. •

Limitations of traditional FMEA in risk ranking were overcome using fuzzy decision

EP

making system (FDMS) and Grey relation analysis. The risk ranking results so obtained

AC C

are compared for better and intelligent decision making of risky components of the considered system.

•

Results are supplied to plant personnel for planning the suitable maintenance policy for the considered system.