On-Line Performance Estimation and Condition Monitoring Using Neuro-Fuzzy Techniques

Copyright 0 IFAC On-Line Fault Detection and Supervision in the Chemical Process Industries, Lyon, France, 1998

ON-LINE PERFORMANCE ESTIMATION AND CONDITION MONITORING USING NEURO-FUZZY TECHNIQUES

Geert Uytterboeven, Jean-Michel Renders, Fran~oise de Viron, Michel De V1aminck

TRACTEBEL ENERGY ENGINEERING, BELGIUM

Paolo F. Fantoni

IFE OEeD Halden reactor Project, Norway

Abstract : Condition monitoring and performance estimation of a system or component means to assess the current state and estimate the future state of the system or component, using real time measurements and calculations. the difference between current and optimal conditions. caused by degradation. are used to evaluate the need and the time for maintenance, where the cost-benefit ratio for the operation is the lowest possible. Increase of availability. reliability and life time are the reasons why condition monitoring should be the preferred maintenance strategy. This paper shows the advantages of neuro-fuzzy based systems for plant monitoring and estimation, by presenting three state-of-the-art applications: ANNODIN (an early fault detection system). OCEAN (Optimisation of combustion and gas emission) and PEANO (Process signal validation and estimation). Copyright@ 1998 !FAC

1. INTRODUCTION

The concept looks simple : build a highly-realistic yet affordable process simulator which, based on known input parameters, can mimic in real-time the entire process (without its possible shortcomings and deviations), and compare this simulator's output to the output of the actual process. If the discrepancy between the two gets significant, then a warning is given to the operator. The advantages of neural network technology, which include: • no need for ''perfect'' analytical processmodelling • learning by examples

Conventional process monitoring & control systems provide . the operator with information based on simple signals issued from the process instrumentation. Such system can react only when an alarm threshold has been exceeded, and only after this it could assist the operator make a diagnosis and initiate the appropriate corrective action. Since "preventing is better than curing", the necessity was perceived for systems that would be capable of detecting a potential problem well before the conventional alarm system operates.

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quick response time compared to conventional analytical simulation methods, resulted in the decision to build the affordable highly-realistic model with the help of neural networks.

describe the behaviour of the subsystem) and minimal (Le. without variable which linearly or nonlinearly depends on already chosen variables); (iii) the subsystem and the associated state variables must react significantly to certain types of faults. The choice of these relationships will be based on physical and experimental considerations: starting from a particular subsystem, we will isolate some state variable (Yi) which we hope to be sensitive both

2. DEJECTION OF INCIPIENT FAULTS ANNODIN (Artificial Neural Networks fOr Detection of Incipient Nuisance) (Renders, et al., 1995 ; de Viron, et al., 1995) is an early detection system aimed at supervision of complex industrial processes or equipment, capable of "feeling" all kinds of nascent symptoms enough time before they materialise and before they can be detected by the operator or give rise to a conventional alarm. The system is aimed primarily at detecting malfunction, allowing predictive maintenance to be done, thanks to timely warning about abnormal wear, fouling, leakage, measurement drift, ...

to certain variables (Xi) and to certain faults. Qualitative physical reasoning allows us to discern variables Xi necessary to predict the behaviour of Yi' but often these variables are not present in our available database. Moreover, the roughness of the qualitative reasoning results in neglecting or overlooking some phenomena. In other words, it is not always possible in practice to ensure the properties of completeness, minimality and sensitivity of the chosen variables and subsystems. Consequently, a complementary approach, more heuristic or more experimental, could be necessary in order to find a satisfactory (as far as completeness, minimality and fault sensitivity are concerned) set of variables Xi, which allows to correctly predict Yi. In a practical way, we have chosen to add to the state variables already chosen by physical considerations, other state variables which produced a significant decrease of the prediction error on Yi' Then, according to the principles of the "analytical redundancy" methodology, the discrepancies between the networks' predictions and the actual process measurements result in "residuals" whose values are the contribution of the process noise, the measurement errors, the modelling errors and the possible faults. A suitable local decision logic is developed, which combines statistical tests and filters, in order to discriminate the incipient fault from the other components of the residual. Additional information regarding the confidence level and the validity of each ANN-model for the current state of the process are also integrated in the decision logic, resulting in a better processing of the "ignorance" of neural networks and a more reliable localisation of the fault. Then, based on the entire set of local decisions (one for each subsystem), a pre-diagnosis is established, taking into account the physical relationships between the subsystems (this is a benefit of our system having been split in several small physical subsystems). The following diagram briefly summarises the different steps of the method.

2.1. The principle and the architecture o/the system

The fault detection methodology is based on the following principles: • The detection of faults is based on the modelling of the physical (thermodynamic, chemical or nuclear) processes occurring in the plant and on the analysis of the discrepancy between the real behaviour and the "normal models" by an appropriate decision logic • The neural networks are used for modelling physical relationships (for instance mass balances, energy balances, ... ) which govern the evolution of some state variables of the plant. In a certain sense, one attempts to exploit the a priori knowledge of the system, even if it is rough and incomplete, by isolating physical subsystems and by associating to each of them a set of variables which one hopes to be necessary and sufficient to describe the subsystem behaviour with a satisfactory accuracy. • The choice of the relationships to be modelled follows our principle of isolating several subsystems of the complex plant and of determining in a suitable way the state variables which allow a correct description (prediction) of the behaviour of each subsystem to . be made. The separation into independent subsystems provides also the advantage of making easier the isolation of the fault and, therefore, its diagnosis. Indeed, the analysis of the residuals associated with each network, and consequently with each subsystem, allows to isolate the faulty component. The selection of variables is of course not unique, but it must adhere as much as possible to the following guidelines: : (i) the variables used must be present in the training data base; (ii) ideally, the set of chosen variables should be complete (Le. sufficient to

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First a set of tools was developed for automating as much as possible the tasks relating to extraction of all the data, pre-processing, building of the models and evaluating them. In particular, the following tools were developed : optimised filtering tools that can attenuate as much as feasible the noise in the measurements, affecting as little as possible the useful signal; data selection tools enabling to select among all the available data a sufficiently small number of representative samples, in order to provide the learning set; statistical processing tools for the «outliers»; model construction tools that facilitate the selection of inputs/outputs and the structure of the modeL Thanks to these tools some sixty models were identified and validated on the basis of real process data, taking the same breakdown into subsystems and functional relations (thermal inventory, mass inventory, ...) as was previously adopted when using the simulated data. The results obtained so far are encouraging: the first conclusions as to the feasibility, drawn from experiments with simulation scenarios, seem to demonstrate that extrapolation to real scenarios is possible despite the difficulties encountered during this change (simulation ..... reality).

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Figure I . Schematic diagram of the method.

2.2. ANNODIN tested on the nuclear plant simulator

2.4. Interface

First, ANNODIN was tested on the full-scope simulator of DOEL 4 at the Scaldis Training Center near Antwerp in Belgium. ANNODIN had been trained to model the secondary system of Unit 4 of Doel nuclear power plant. With 94 neural networks some 30 components are simulated, including heat exchangers, a turbine, a condenser, .... These models were trained on the data-network at nominal power under normal plant operation conditions, i.e. without failures, incidents or accidents. After this a variety of scenarios were enacted that included simulation of small local leaks and gradual clogging of filters. The results were found good to excellent: ANNODIN detected small leaks and fouling long before the conventional alarms went off, and there were not too many false alarms.

A special Engineer-Machine Interface has been developed, in order to validate the above methodology (this is obviously not the final MM! presented to the operator). The normalised residuals (=residuaVconfidence interval) of the 94 models, as well as the validity indices of these models, are displayed at each time (see Figure 2). ASS

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2.3. ANNODIN on the real power station During three months some 160 process variables were recorded, with a sampling frequency of 12 per hour. ANNODIN was re-trained on part of these recorded measurements, and tested on another part. The main purpose was to demonstrate that the ANNODIN methodology, which showed excellent results when using the simulated data, was still valid when used on the real process, despite the many differences this entailed (measurement noise, complex phenomena not accounted for in the simulator, missing data, ... ).

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Then, one particular model can be selected in order to see which subsystems it is dealing with (see figure 3) and what is the evolution of the model results (residuals, confidence interval, validity index) in order to validate the sensitivity to known incidents and the insensitivity to other "normal" perturbations (see figure 4).

Increasingly strict environmental regulations and fiercer competition have forced the operators to react as quickly and as adequately as possible to any external variations, so as to retain the energyefficiency and meet at any time all the relevant regulations. OCEAN, which is a neural-network based model, supplies advice to the operator so that he may optimise the process. Just like with ANNODIN, a neural model will be built that mimics the combustion process. Downstream of this model a number of optimisation algorithms are installed so that the system can always be used in two ways: • either as a 'process-optimiser' : OCEAN supplies the optimal position of all the combustion-air control valves, the speed of the coal mills, the fuel feed rates, ... depending on what the operator intends to optimise more particularly in function of outside constraints or policy with respect to fuel quality, load, . . .. The first target is the reduction in NOx emission, but this can be extended to optimisation of the energy efficiency and/or the quality of the fly-ash either as a simulator, allowing a "what-if' analysis, to investigate the effects of various factors on the combustion efficiency and emissions (influence of the coal quality, register positions, burner configuration, ...).

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3.2. The preliminary study The preliminary study was aimed at checking whether a neural network based optimisation system is feasible both technically and financially, forming an idea of the possible problems that could arise, taking a look at other fossil-fired power stations where development of similar projects is in progress and, finally, performing a market survey concerning existing software packages that could be suitable.

Figure 4. Typical evolution of the residual and model validity at the beginning of the fault 3. PROCESS OPTIMISATION

4. SIGNAL V ALIDAnON

OCEAN (Optimisation of Combustion and Emissions with Artificial Neural Networks) is a project aimed at optimising combustion in fossil-fired power stations with a view to reducing the NOx emissions, taking into account the limit conditions regarding fuel quality, efficiency and fuel residue in the fly-ash. This project is being developed with the collaboration of two Belgian companies : Electrabel and Laborelec (the Laboratory of Electrabel).

Artificial Neural Networks and Fuzzy Logic models can be combined to exploit learning and generalization capability of the first technique with the approximate reasoning embedded in the second approach. Real-time process signal validation is an application field where the use of this technique can improve the diagnosis of faulty sensors and the identification of outliers in a robust and reliable way.

3.1. The principle

The PEANO system implements a fuzzy and possibilistic clustering algorithm to classify the operating region where the validation process has to be performed. The possibilistic approach (rather than probabilistic) allows a "don't know" classification that results in a fast detection of unforeseen plant conditions or outliers. Specialized Artificial Neural Networks are used for

In a fossil-fired power station there exist a few hundred parameters with which the combustion can be adjusted. Until now, of all these parameters only some 30 are actually used to control the combustion process in normal operation (mainly for adapting to load change) or to adjust to a variation in fuel quality.

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the validation process, one for each fuzzy cluster in which the operating map has been divided. They work concurrently on each signal pattern presented to the system and the overall contribution is weighted according to the membership function of the pattern in each cluster. There are two main advantages in using this technique : the accuracy and generalization capability is increased compared to the case of a single network working in the entire operating region, and the ability to identify abnormal conditions, where the system is not capable to operate with a satisfactory accuracy, is improved. Figure 5.1 shows a functional diagram of PEANO, with the bank of ANN and the reliability model driven by the classifier.

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PEANO has been recently tested (P.F. Fantoni, S.Figedy, B.Papin, 1997 P.F. Fantoni, S.Figedy,A.Racz, 1998) successfully using data from a Pressurized water nuclear reactor in France. Figure 5.3 shows how a drift in the Primary loop water temperature is detected, as soon as the drift exceeds O.2e.

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The same figure gives an idea of the accuracy of the system (represented by the the two bands in red), which is a consequence of the use of specialized ANN ineach operating region of the process. PEANO will be installed in a nuclear full-scope training simulator in the second half of 1998, where tests in real-time will be performed.

Figure 5.1 PEANO functional diagram

5. CONCLUSIONS Neural Networks and Neuro-Fuzzy systems for process monitoring and diagnosis have been the object of an extensive research and testing, in the last ten years. The results achieved with emerging system implementations, like those described in this paper, suggest that this technology is mature for industrial implementations. The following two considerations, in the authors opinion, will encourage and booster industrial applications of this technology in the near future:

5.1. The technical aspect The experiments and experience with neural network applications in complex industrial environments lead us to the following conclusions : the approach based on using a priori knowledge (of the physical laws of the process) seems sound and quite effective;

Figure 5.2 The PEANO display in monitor mode

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the development methodology for process supervision, diagnosis or optimising systems, using neuro-fuzzy techniques, which the authors developed and experimented, is feasible and reliable and is suitable in many industrial contexts, such as petro-chemical industries.

5.2. The point of view of the process operator or the plant maintenance planner In an environment where competition gets ever fiercer and where safety and economic criteria are paramount, tools that can help optimise the process during normal operation or tools that can help timely planning of preventive maintenance, are in great demand and prove more and more valuable. REFERENCES

J.M. Renders, A. Goosens, F. de Viron en M. De Vlaminck, HA prototype neural network to perform early warning in nuclear power plant" Fuzzy Sets and Systems 74 (1995) 139-151 F. de Viron, M. De Vlaminck, J.M . Renders, "Artificial Neural Networks in process monitoring: a practical experience with a prototype to perform early warning in nuclear plants", paper presented at the International Conference on Artificial Neural Networks, Paris, October 9-13, 1995, France P.F. Fantoni, S.Figedy, B.Papin, HA Neuro-Fuzzy Model Applied to Full Range Signal Validation of PWR Power Plant Data", Second OECD Specialist Meeting on Operator Aids for Severe Accident Management (SAMOA-2), Lyon, 8-10 September 1997 P.F.Fantoni,S.Figedy,A.Racz, "PEANO: A TooBox for Process Signal Validation and Estimation ", HWR-515, OECD Halden Reactor Project, February 1998

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