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A Knowledge-Based System with Embedded Estimation Components for Fault Detection and Isolation in Process Plants
Copyright © !FAC On-line Fault Detection and Supervision in the Chemical Process Industries. Delaware. USA. 1992
A KNOWLEDGE-BASED SYSTEM WITH EMBEDDED ESTIMATION COMPONENTS FOR FAULT DETECTION AND ISOLATION IN PROCESS PLANTS Z. Fathi*, W.F. Ramirez*, A.P. Tavares*, G. Gilliland** and J. Korbicz*** *Department a/Chemical Engineering. University a/Colorado. Boulder. CO 80309. USA **Public Service Company a/Colorado. 1667 Cole Boulevard. Golden. CO 80401. USA ***Department 0/ Applied Mathematics and Computer Science. Higher College 0/ Engineering. ul. Podg6rna 50. 65-246. Zielona G6ra. Poland
Abstract. Fault diagnosis in the area of process operations is critical for modern production and is receiving increasing theoretical and practical attention. In spite of many research and practical attempts, process fault diagnosis remains a rather complex task. In this work, we present a diagnostic methodology in which the symbolic reasoning of knowledge-based systems techniques is integrated with quantitative analysis of analytical redundancy methods. The system first performs a diagnosis by means of a compiled knowledge structure, and then attempts to build a detailed explanation by using proper fault models (adaptive filters). It also performs various statistical analyses to determine the process condition and check the validity of models. This unified approach increases the completeness and reliability of the diagnostic system. Keywords: failure detection; estimation ; Kalman filters ; expert systems; hierarchical systems.
strategies. The reasoning can be compiled (Kumamoto and co-workers, 1984) or model- based (Rich and Venkatasubramanian, 1987). The first approach offers efficiency and effectiveness while the latter offers flexibility and accuracy. The importance of integrating the two types of reasoning has been discussed by Kramer (1987) and Venkatasubramanian and Rich (1988).
INTRODUCTION The complexity of most automatic control systems for contemporary technological processes in the chemical industry, power engineering and many other areas demands the solution of reliability problems. Traditionally, fault-tolerance is achieved through the use of hardware redundancy, pattern recognition, information flow graphs, and analytical redundancy (Himmelblau, 1978). However, the latest advances in the area of artificial intelligence (knowledge-based systems and neural nets) allow us to develop new approaches to fault detection and diagnosis in dynamic systems (Tzafestas, 1989; Naidu and co- workers, 1990). The main advantage of the knowledge- based approach lies in the fact that it complements the existing analytical methods and makes use of the qualitative models based on the available knowledge of the system.
This work presents an integrated approach based on combining the analytically-redundant FDI schemes and the knowledge-based techniques. The overall structure of our diagnostic methodology of embedding estimation-based techniques within the framework of a knowledge base is represented in Figure 1. The raw data are fed to a fault detector (a preprocessor) which performs statistical tests to identify the process condition (normal or abnormal). The preprocessor is basically an alarm system that is used to trigger the initiation of the knowledge-based system. It also transforms the numerical data to qualitative values. The task of the knowledge base is to determine the source and extent of the true fault . Both a state/ parameter estimator and a statistical analyzer are included in the loop of diagnostic reasoning . The design methodology is a hierarchical knowledge structure with compiled knowledge at higher levels of abstraction and analytical redundancy at lower levels of abstraction . This integrated approach reduces the computational complexity associated with functionally-redundant schemes and increases the effectiveness of the knowledge-based approach.
Analytically- redundant fault detection and isolation (FDI) schemes are basically signal processing techniques employing state estimation, parameter estimation, adaptive filtering, variable threshold logic , and statistical decision theory. Over the past two decades, basic research on FDI using analytical redundancy has evolved considerably due to the emergence of powerful techniques of mathematical modeling, state estimation, and parameter identification (Patton and co-workers, 1989; Frank, 1990). However, the complexity and the prerequisites of this approach have precluded the widespread application of these techniques.
The model-based redundancy issues have been addressed in an earlier paper (Fathi and co-workers, 1992). In this current paper, the physical system, the analytical redundancy problems, and the statistical decision methods are first briefly described. The knowledge base design and issues related to integration and implementation are then discussed. A simulation result illustrating the potency of the integrated diagnostic approach is finally presented .
The knowledge- based approach to FDI, over the past decade, has received considerable attention (Tzafestas , 1989). This approach can be quite effective in tackling the problem of diagnosis. It provides the means and flexibility in gathering and organizing the various types of knowledge present in process operation. The main issues addressed in this area of research are knowledge representation and problem-solving
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The augmented state vector, z, and the output vector, y . • )f these filters are defined as
PROCESS DESCRIPTION The process schematic shown in Figure 2 represents tl,,· plant components and flow paths selected for prototype d,·· agnostic system development. This diagram shows the condensate pump, the control valve, the deaerator level controller, the extraction steam pipe, and the deaerator and its storage tank. It does not include the low- pressure feedwater heaters; they are represented only as flow resistance between the control valve and the deaerator. The objective in this work is the analysis of problems associated with the condensate pump , the transportation lines, the deaerator, and its control system. The following gives a brief description of the tasks that the various components perform.
Filter I Filter II
zT zT
= [wv
Ceq )
= [Pp w v )
,
y
= [wv )
(1)
,
y
= [Pp )
(2)
In Filter I , WV (the mass flow rate of water leaving valve) is the state variable and C eq (the equivalent conductance) is the unknown parameter while in Filter II, Pp (the pressure of water entering valve) is the state variable and Wv is the unknown parameter. Filter I simulates a fault model corresponding to a change in conductance (e.g., fouling) and Filter II models a fault that manifests itself as a change in flow (e.g., leak, flow sensor failure).
The condensate pump imparts kinetic energy to the pumped fluid from the hotwell of the condenser which is then recovered as stored energy at a higher fluid outlet pressure. The valve is primarily used for modulating to control the flow rate through the valve for maintaining the liquid level in the deaerator storage tank . The extraction pipe is the interconnecting component between the turbine and the deaerator through which the extraction steam flows. The deaerator level controller is a three-mode proportional- integral controller with anti-reset windup.
For the extraction pipe, the augmented state vector and the output are defined as y
= [Pe )
(3)
Here, the desired state variable is the steam pressure at the outlet of the extraction pipe, Pe, and the unknown parameter to be estimated is the steam mass flow rate , We ' This filter simulates a fault model corresponding to a low or high input steam flow to the deaerator.
The deaerator subsystem is composed of a deaerator (an open feedwater heater) and its storage tank. Deaerators serve four major tasks: (1) removal of non condensable gases to prevent corrosion and scaling of boiler surfaces due to gases dissolved in the feed water, (2) heating of feedwater, (3) provide feedwater storage, and (4) are located to provide a substantial suction head on the boiler feed pumps, preventing pump cavitation by supplying a substantial net positive suction head.
In the filter considered for the deaerator, the states and outputs are defined as
(4) The specific bulk enthalpy, hd, and the deaerator pressure , Pd , are the state variables and the output mass flowrate , Wd •• " is the unknown parameter. The measured variables are the deaerator pressure, Pd , and the liquid level in the storage tank, L. This filter is designed to detect output flow sensor failure (this sensor is in the loop of the deaerator lev(·l control system). The faulty mode is indicated by the dis crepancy between the estimated and measured quantities.
The components of the power plant subsystem have been modeled through the use of MMS (Modular Modeling Sys tern) documents obtained from EPRT (Energy and Power Research Institute). The mathematical model 10r each com ponent consists of conservation of mass, conservation of ell,.rgy, fluid mechanics, and fluid properties (Fathi and coworkers, 1992).
Another detection filter based on the deaerator model was considered for the level sensor failure. In this model, enthalpy and pressure were defined as states and pressure as the only measured quantity. A linear time-invariant approximation of the model was used to study the observability properties of the state-space model. The enthalpy was found to have very poor observability behavior. This was revealed through examining both the observability matrix and the observability gramian. The alternative is to use an autoregressive moving average model for the level sensor. This is a quite attractive approach which can in general be used for sensor validation at the highest level of troubleshooting (Yung and Clarke, 1989) .
ANALYTICALLY- REDUNDANT APPROACH To date , many FDI schemes based on the mathematical process models, estimation techniques, and statistical decision algorithms have been proposed for the stochastic and deterministic dynamic systems . Most of the work considering the problem of FDl from the point of analytical redundancy proposes to use the Kalman filter in the stochastic case, and the Luenberger observer in the deterministic case. Here, we base the design of our detectors on two concepts: structural decomposition and adaptive Kalman filtering . That is, the plant structure is decomposed into units for which individual filters are designed. In general, the design of local filters can range from one incorporating most likely fault modes to one incorporating all possible ones. To cover a broader range of practical domains, the modified extended Kalman filter which is designed for the joint state and parameter es timation in systems with coupled slow and fast dynamics is considered (Fathi and co-workers, 1991) .
For the controller, the filters are designed according to
= [Of ZT = [Of ZT
= lOT) Y = lOT )
k[ ) , y kp )
,
(5)
The integrator output, 0[, is the state variable. The unknown parameters are considered to be the integrator gain , k[, and the proportional gain, kp • The measured quantity is the controller output, OT, which is the sum of proportional and integrator outputs.
I n this section, the failure detection of the condensatefeed water subsystem of a coal-fired power plant is considered based on the analytical red undency approach. To design the local detection filters , the system shown in Figure 2 is decomposed into: condensate pump, control valve, extraction pipe, deaerator , and controller. In the following , the individual filters are briefly described . Further details on estimation algorithm and filter formulation can be found in another paper (Fathi and co-workers, 1992).
STATISTICAL DECISION METHODS Several statistical tests for the on-line detection of shifts in measurable output signals, signal trends, and characteristic quantities are considered (Basseville and Benveniste, 1983). Under normal operating conditions, the observations follow some known evolutionary patterns within certain limits of uncertainty introduced by random process disturbances and measurement noise . When faults occur, these variables deviate from their nominal trajectories. The signal derivative also permits a certain prediction of the signal progression and thus could lead to an earlier fault detection. Testing nonfiltered signal derivatives has poor performance due t o
For the deaerator level control valve (and the piping associdted with the low-pressure heaters), two filters are designr>r:I.
44
the noisy nature of the signals. However, if the signal is first filtered through a window of certain length, the statistical tests can be applied on the derivative of the filtered signal. Furthermore, the customary checking of the characteristic quantities provides essential information when supervising large process plants. These quantities , which are determined from input- output data , can also be used for detection of faults.
Our methodology is based on the use of analytical redundancy approach to FDI in the context of a knowledge base. The advantages that this integrated approach offers can be viewed from two points. First, the analytically-redundant a.pproach • is fundamentally a diagnostic technique , • can facilitate the task of diagnosis by providing additional sources of information, • allows natural use of hierarchical representation methods due to flexibility in the level of description (a marked advantage for dealing with large and complex domains), and • makes knowledge-based systems more reliable by allowing systematic isolation of faults.
A commonly used technique for the on- line detection of shifts is limit checking. One scheme, without counter, releases an alarm or warning as soon as the signal falls outside a certain tolerance of the normal value. The other schemes, with counters, detect when the number of limit crossings or the crossings of the same direction, during a time duration, exceed a certain number. A more elaborate method than limit checking is the cumulative sum procedure. It depends on the past history of the process and thus can detect a relatively small change in the process mean.
Second, in comparison with the well-known methods of functional redundancy (Patton and co- workers, 1989) that require the on-line implementation of a series of estimators based on different mathematical models and measurement subspaces, our approach provides a more effective and efficient way of employing the filter modules through the use of compiled knowledge. In other words , when an abnormality appears, a guided situation-specific filter is activated instead of a bank of filters being implemented (a computationally inefficient approach). Also since the filter is activated only when a potential problem exists, it operates in its most sensitive state of dynamic operability.
A statistical procedure used for the detection of changes due to unmodeled phenomena is also considered. When the knowledge base inquires for information from the estimation modules , a situation-specific filter is implemented. However , the validity of the suggested filter should be examined through statistical methods. A variety of statistical tests can be performed on the innovations or resid uals to determine the validity of the model used in the filter design . To monitor the measurement innovations (or residuals) , we use the modified Sequential Probability Ratio Test (Chien and Adams , 1976).
Knowledge Structure The overall objective in developing a knowledge-based system is to capture both the domain knowledge and the problem-solving strategy. Thus, an essential element in knowledge- based system development lies in understanding the problem in terms of knowledge, organization of knowledge, and reasoning strategies. Implementation of these organizational and reasoning strategies requires additional problemsolving control that has also important implications on the degree of modularity for maintaining and upgrading the knowledge- base (Davis and Gandikota, 1990).
KNOWLEDGE- BASED APPROACH Knowledge- based approaches are strongly oriented to problems of fault diagnosis. In process plant operations, a significant layer of human problem-solving method is recognized to be a knowledge- rich domain in terms of human expertise. The knowledge- based system techniques allow the symbolic reasoning of problem- solving experts to be incorporated into automated systems.
Rasmussen (1985) has discussed the role of hierarchical knowledge representation in decision making. From the controller's point of view, the complexity of a system is not an objective feature of the system and depends upon the resolution applied for information search . The control of complex systems (large number of information sources and devices) can be coped with by structuring the situation and thereby transforming the problem to a level with less resolution. From analysis of verbal protocols, Rasmussen observed this structuring in two dimensions: whole/part dimension reflecting various levels of physical decomposition and abstract / concrete dimension reflecting various levels of functional abstraction (highest level representing ultimate purpose of the system and lowest level representing physical form of the system). The role of abstraction at proper levels is to reduce the observed complexity of the system under consideration. In the space of whole/ part and abstract / concrete dimensions, the human diagnostic reasoning moves from a high level, where functional purpose of the whole system is considered, to a detailed level, where the physical form and function of the individual components are analyzed. These human organizational strategies and conceptual abstractions suggest a hierarchical representation of knowledge. The hierarchical structure offers efficient topdown solution procedures for classification-type problems.
There are some issues in regard to the previous work that need to be mentioned. Early applications of symbolic reasoning to process fault diagnosis centered around symptombased pattern recognition methods to FDI. The difficulties inherent in this approach were recognized to be lack of flexibility and reliability under novel conditions. Later work attempted to use some form of model which captures and organizes deep-process knowledge to predict system's behavior. A number of modeling methods associated with the use of knowledge- based reasoning systems may be classified as:
• Constraint- based models: Rules are derived from constraints imposed on process behavior by the physical laws (e .g., method of governing equations). • Logic-based models: This approach is generally in the form of a geometric struct ure (information flow graphs) which relates parts of the process and defines paths of causality for events occurring in the process (e.g., fault trees , signed directed graphs). • Qualitative physics: This method infers the system's behavior from knowledge of its structure (states and physical laws governing their changes). Unconstrained model- based reasoning was found to be computationally inefficient. To remedy this problem, compiled problem-solving components were integrated into deep knowledge approaches. More recently, the work in this area is directed toward blending partially-quantitative knowledge and qualitative reasoning in the framework of knowledge bases in order to overcome some of the shortcomings of the qualitative approaches (ambiguities and spurious solu lions). In spite of the many advances in diagnostic problemsolving, there still exist need and potential for further re-
An appropriate domain concept for context grouping of diagnostic knowledge in a hierarchical structure is malfunction hypothesis (Chandrasekaran, 1984, 1986). Thus, to provide a necessary general structure for the knowledge base, a malfunction hierarchy is identified for organizing knowledge about the system (Shum and co-workers, 1988). The use of malfunction hypothesis as the conceptual basis for constructing the hierarchy provides an efficient problemsolving framework , allows for a natural decomposition of the
"~arch.
45
licient, integrated medium for our knowledge- based system development.
domain for focus and control, and supports organized and efficient knowledge-acquisition. In addition, this approach provides a flexible structure for including and efficiently focusing the quantitative considerations of analytical redundancy techniques.
In our knowledge- based system, the domain is modeled in terms of objects, classes , and properties. The specific properties of objects and classes form the slots. Slots are used to store any information that comes into the knowledge- based application. For the condensate-feed water system, a knowledge base that defines the entities of the system and their characteristics in terms of classes, objects, and properties has been created. Figure 4 represents a small portion of the object network. This network has been defined using engineering knowledge on primitive concepts of the domain.
The construction of the malfunction hierarchy involves the identification of functional systems / subsystems in the process. Figure 3 represents the malfunction hierarchy generated using the system knowledge for the condensate-feedwater subsystem of a power plant. Higher levels are related to more general functional segments and physical subsystems and lower levels are related to more specific functional units and physical components. The hierarchical structure allows the decision process to proceed from a high level of generality to a high level of detail.
To represent reasoning over the object network and thus capture the knowledge necessary to solve the diagnostic problem , we use rules . For the condensate-feed water system, a knowledge base that contains the problem-solving knowledge in terms of rules has been created. This knowledge base has been developed through preliminary knowledge acquisition based on background knowledge and use of documen ts such as A nalog/ Digital Data Point and Instrumentation Logic Diagrams provided by Public Service Company, and detailed knowledge acquisition based on meetings with plant experts. Since the hierarchical malfunctiondriven approach exploits the concept grouping used by experts, the knowledge acquisition was substantially facilitated.
The diagnostic knowledge employed at the higher nodes of the hierarchy (more general malfunction hypotheses) is compiled while that at the lower nodes (more specific malfunct.ion hypotheses) makes use of estimation-based techniques . In this manner , initially the compiled knowledge is used to rapidly narrow down the solution space and provide diagnostic focus. Then, the filter modules are triggered to resolve and refine the suspected malfunction hypotheses in the reduced and more focused search space. This integrated approach increases the completeness and reliability of the diagnostic system.
To illuminate the following discussion , a few basic concepts of the knowledge processing are described. The mechanism by which events are processed is a dynamic, priority-based scheduling system (agenda). There are several types of inference search mechanisms such as backward chaining, hypothesis forward , semantic gates, context links, etc. with different priorities. Moreover, the inference strategies can be disabled or limited in scope globally or locally.
Qualitative / compiled knowledge captures directly the efficiency of domain experts in generating plausible hypotheses and serves to focus diagnostic attention on a small set of subsystems. Estimation-based methods that resort to fun damental analysis provide the rationale for a qualitativelyguided reasoning process. Reasoning with filter models in explaining transient behavior under faulty conditions allows the set of qualitatively- generated hypotheses to be resolved :lnd refined.
To modularize the knowledge base and offer more flexibility, the compiled knowledge used to obtain a diagnostic focus is weakly linked to the rules activating the quantitative analysis via context links (the lowest level event on the agenda). The weak link allows the quantitative analysis to be performed only after obtaining a focus of attention, triggered independently, or activated through other knowledge sources.
The domain knowledge used at higher nodes of the hierarchy is compiled and qualitative. This knowledge, which is obtained from domain experts, are organized high-level abstractions derived from detailed models or explicit causal reasoning, and / or developed through experience. The fact that the compiled knowledge used in the knowledge base has been derived from expert diagnosticians does not say anything about the origin of the knowledge (associationally, experientially, or causal-based; Chandrasekaran, 1991).
To provide a graphics-based user interface, MODEL and Nexpert Object are run under Microsoft Windows. The two media are interfaced through the Dynamic Data Exchange (DDE) calls of Microsoft Windows. Figure 5 depicts the software environments for various system components and how they are interfaced. In the current version of the prototype detection and diagnosis system, the inputs are simulated . These inputs are statistically tested to determine the plant condition. In case of detecting an abnormal behavior, the transformed qualitative quantities are sent to the knowledge base. Then, the top-level hypothesis in the compiled knowledge island is suggested and the knowledge processing is activated. When a diagnostic focus is obtained, the hypothesis of the rule activating a respective filter is put on the agenda. The first condition in this rule turns on the flag that triggers the estimation module. The next condition that checks the adequacy of the fault model needs the object slot whose value is determined via a statistical test in the filter module. A method is attached to the slot of this object that causes the inference to be suspended until it is resumed again by the client through the DDE calls (methods can be attached to the object slots to obtain a needed value or perform an action if the value in the slot changes). While the knowledge processing is suspended, the filter is run using a window of past and current data including last data transfer (data are saved in a history file) . The object slots containing the filter status and result are then sent through DDE calls to the knowledge base and the inference is resumed. The estimation results are tested, filter activati on flag is turned off, and finally conclusions are displayed
IMPLEMENTATION AL ENVIRONMENTS An ideal support base for developing computer- aided process engineering software would meet the requirements of a modular software development style, a uniform environment, and powerful graphics tools. Object- oriented programming lends itself well to these needs (Stephanopoulos , 1987). The development of our diagnostic system, which requires support from a multitude of generic and specific software facilities , is based upon this programming paradigm. The medium for performing numerical processing tasks such as simulation, statistical analysis, and estimation modules has been identified as MODEL (Model Software) . MODEL is a high-level, object- oriented engineering language that provides solutions for real-time distributed process simulation and control. Task characteristics naturally influence the choice of a tool for knowledge base development. The key is to choose a tool that has the feat ures suggested by the needs of problem and application. However, some of the most recent tools incorporate a multitude of knowledge representations and control techniques and are made to be appropriate for more types of problems. Nexpert Object (Neuron Data), a C- based, object- oriented, hybrid tool with a knowledge representation paradigm based on rules and objects constitutes an d·
46
An Example
niques to tasks. In W. Reitman (Ed.). Artificial Intelligence in Business, Ablex Publishing. Chandrasekaran, B. (1986). Generic tasks in knowledgebased reasoning: High-level building blocks for expert system design . IEEE Expert 1(3), 23-30. Chandrasekaran , B. (1991). Models versus rules, deep ver sus compiled , content versus form-some distinctions in knowledge systems research. IEEE Expert 6(2) 75-79. Chien, T. T. and M. B. Adams (1976). A sequential failure detection technique and its application. IEEE Trans . on Automatic Control AC-21, 750-757. Davis, J. F. and M. S. Gandikota (1990). Rule-based Systems in Chemical Engineering. In G. Stephanopoulos and J . F . Davis (Ed .). CACHE monograph series , CACHE, Austin, Texas. Fathi, Z., W. F. Ramirez, and O. Aarna (1991). A joint state and parameter estimator for systems with coupled fast and slow dynamic modes. AIChE Annual Meeting, Los Angeles, California, November 17-22. Fathi , Z., J. Korbicz, W . F. Ramirez, and G. Gilliland (1992). An integrated diagnostic system using analytical redundancy and artificial intelligence for coalfired power plants . IFAC Symposium on Control of Power Plant Systems, Munich, Germany, March 912. Frank, P. M. (1990). Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy. A survey and some new results. A utomatica 26(3), 459-474. Himmelblau, D. M. (1978). Fault Detection and Diagnosis in Chemical and Petrocl!tmicd Processes. Chemical Engineering Monograph 8, Elsevier Scientific, Amsterdam. Kramer, M. A. (1987). Malfunction diagnosis using quantitative models with non - Boolean reasoning in expert systems. MChE J. 33(1), 130- 140. Kumamoto, H., K. Ikenchi, and K. Inoue (1984). Application of expert system techniques to fault diagnosis. Chemical Engineel'ing Journal (Biochem. Eng. J.) 29(1), 1-9. Model Software. Solutions for Real Time Simulation and Control. 52 Curtis Court, Broomfield, CO 80020 . Naidu, S. R., E. Zafiriou, and T. J. McAvoy (1990). Use of neural networks for sensor failure detection in a control system. IEEE Control Systems Magazine 10(3), 49-55. Neuron Data. 444 High Street, Palo Alto, California 9430l. Patton, R., P. Frank, and R. Clark (1989). Fault Diagnosis in Dynamic Systems. Theory and Applications. Prentice Hall, New York. Rasmussen, J. (1985). The role of hierarchical knowledge representation in decisionmaking and system management. IEEE Transactions on Systems, Man, and Cybernetics SMC-15(2), 234-243. Rich, S. H. and V. Venkatasubramanian (1987). Modelbased reasoning in diagnostic expert system for chemical process plants. Computers €3 Chemical Engineering 11, 111-122. Shum, S. K ., J. F. Davis, W. F. Punch Ill, and B. Chandrasekaran (1988). An expert system approach to malfunction diagnosis in chemical plants. Computers €3 Chemical Engineering 12(1), 27-36. Stephanopoulos, G. (1987). The future of expert systems in chemical engineering. Chemical Engineering Progress 83(9), 44- 5l. Tzafestas , S. G . (Ed.) (1989). Knowledge-Based Sys tem Diagnosis, Supervision and Control. Plenum, New York. Venkatasubramanian, V . and S. H. Rich (1988). An objectoriented two- tier architecture for integrating compiled and deep-level knowledge for process diagnosis . Computers €3 Chemical Engineering 12(9), 903-92l. Yung, S. K., and D. W. Clarke (1989). Local sensor validation. Measurements and Control 22, 132-14l.
In order to illustrate the system trail and its efficacy in performing a diagnostic task, an example is presented. In this example, a fault, namely, fouling in the control valve/ condensate line is simulated . The details of the diagnosis are described below. As mentioned previously, in this phase of the project , inputs to the system are generated through simulation. The data are scanned every 10 seconds. To monitor the process , various statistical tests are performed. After some time has elapsed, the pipe conductance is reduced by 20 percent. As this disturbance is introduced, an abnormality is detected and the knowledge base is triggered . After establishing the top level hypothesis (i.e., the condensate feedwater subsystem malfunction), the pump , the level control system, and the deaerator are considered. Note that the more general rules are applied at the class level through pattern matching. The deaerator malfunction is then established and is further refined to investigating the condensate line malfunction. When this diagnostic focus is obtained, through context links the hypotheses of the rules activating the two respective filters (simulating faults in flow and conductance) are put on the agenda. The relevant filter corresponds to a fault that manifests itself as a change in conductance. The first condition simply turns on the filter flag which in turn causes the filter to be activated. A method is attached to the status slot of this object that suspends the knowledge processing until it is resumed again after filter completion. The estimation result is shown in Figure 6. The equivalent conductance (pipe and valve) is a function of valve position. As the flow resistance is increased, the valve opens up in order to prevent the deaerator level from dropping . The expected conductance represents the normal value while the estimated conductance represents the present condition. The fault is determined by examining the normalized deviation between the expected and present values. The final conclusion is then displayed as a process schematic with a warning message. This example illustrates how the analytical redundancy and the knowledge-based systems technique complement one another in providing a coherent and efficient system. This integrated approach increases the reliability and completeness of the diagnostic system.
CONCLUSION A diagnostic methodology in which the symbolic reasoning of knowledge-based systems techniques is integrated with quantitative analysis of analytical redundancy methods has been presented . The analytical algorithms are housed at the low levels of abstraction of a hierarchical knowledge structure. The knowledge at the higher levels is compiled and suggest a situation-specific filter to be employed for fault diagnostic resolution. Such an integrated approach (analytical and knowledge- based redundancy) reduces the computational complexity of the analytical algorithms and increases the effectiveness of the knowledge-based system. The numerical experiments implemented using a subsystem of a power plant illustrated the potency of the integrated approach . Further research is required in considering more elaborate techniques of analytical redundancy and actual system testing.
REFERENCES Basseville, M. and A. Benveniste (1983) . Design and comparative study of some sequential jump detection algorithms for digital systems. IEEE Trans. on Acoustics, Speech, and Signal Processing ASSP-31(3), 521-534. Chandrasekaran, B. (1984). Expert systems: Matching tech-
47
Fault Detector
Knowledge-Based System
Fig. 4.
Overall block diagram of fault detection and diagnosis system
Fig. 1.
A small portion of the overall object network for the condensate- feed water subsystem Dynamic OaU Exchange - send data • suggest top level hypothesIS - start the i{OQ',vledge processing
~
~ --r
.-1
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I I I I
l
-
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Dynamic Data Exchange • send data • start the knowldge processing
Fig. 2.
Condensate- feed water subsystem
Fig. 5.
System interface
4.0 10' , - - - - - - - - - - - - - - - - - - - , 1.00 MeCha.",cal Pump
( Elec trical level Control Valve
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Mechanical - - ( Recirculation Valve
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.0
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~ Upscale Transducer
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~.
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Condensate Parameter eedwater Parameter
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~
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- - Estimated Flow
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2.0 1040'----1'--0--2'--0--3'--0--4'--0--5'--0----'600.50
Extraction line Feedwater line
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Startup Running Orifices
Fig. 6.
Malfunction hierarchy for condensate-feed water subsystem
48
~
Measured and estimated flow, expected and estimated conductance, and valve position versus time- valve module
A KNOWLEDGE-BASED SYSTEM WITH EMBEDDED ESTIMATION COMPONENTS FOR FAULT DETECTION AND ISOLATION IN PROCESS PLANTS Z. Fathi*, W.F. Ramirez*, A.P. Tavares*, G. Gilliland** and J. Korbicz*** *Department a/Chemical Engineering. University a/Colorado. Boulder. CO 80309. USA **Public Service Company a/Colorado. 1667 Cole Boulevard. Golden. CO 80401. USA ***Department 0/ Applied Mathematics and Computer Science. Higher College 0/ Engineering. ul. Podg6rna 50. 65-246. Zielona G6ra. Poland
Abstract. Fault diagnosis in the area of process operations is critical for modern production and is receiving increasing theoretical and practical attention. In spite of many research and practical attempts, process fault diagnosis remains a rather complex task. In this work, we present a diagnostic methodology in which the symbolic reasoning of knowledge-based systems techniques is integrated with quantitative analysis of analytical redundancy methods. The system first performs a diagnosis by means of a compiled knowledge structure, and then attempts to build a detailed explanation by using proper fault models (adaptive filters). It also performs various statistical analyses to determine the process condition and check the validity of models. This unified approach increases the completeness and reliability of the diagnostic system. Keywords: failure detection; estimation ; Kalman filters ; expert systems; hierarchical systems.
strategies. The reasoning can be compiled (Kumamoto and co-workers, 1984) or model- based (Rich and Venkatasubramanian, 1987). The first approach offers efficiency and effectiveness while the latter offers flexibility and accuracy. The importance of integrating the two types of reasoning has been discussed by Kramer (1987) and Venkatasubramanian and Rich (1988).
INTRODUCTION The complexity of most automatic control systems for contemporary technological processes in the chemical industry, power engineering and many other areas demands the solution of reliability problems. Traditionally, fault-tolerance is achieved through the use of hardware redundancy, pattern recognition, information flow graphs, and analytical redundancy (Himmelblau, 1978). However, the latest advances in the area of artificial intelligence (knowledge-based systems and neural nets) allow us to develop new approaches to fault detection and diagnosis in dynamic systems (Tzafestas, 1989; Naidu and co- workers, 1990). The main advantage of the knowledge- based approach lies in the fact that it complements the existing analytical methods and makes use of the qualitative models based on the available knowledge of the system.
This work presents an integrated approach based on combining the analytically-redundant FDI schemes and the knowledge-based techniques. The overall structure of our diagnostic methodology of embedding estimation-based techniques within the framework of a knowledge base is represented in Figure 1. The raw data are fed to a fault detector (a preprocessor) which performs statistical tests to identify the process condition (normal or abnormal). The preprocessor is basically an alarm system that is used to trigger the initiation of the knowledge-based system. It also transforms the numerical data to qualitative values. The task of the knowledge base is to determine the source and extent of the true fault . Both a state/ parameter estimator and a statistical analyzer are included in the loop of diagnostic reasoning . The design methodology is a hierarchical knowledge structure with compiled knowledge at higher levels of abstraction and analytical redundancy at lower levels of abstraction . This integrated approach reduces the computational complexity associated with functionally-redundant schemes and increases the effectiveness of the knowledge-based approach.
Analytically- redundant fault detection and isolation (FDI) schemes are basically signal processing techniques employing state estimation, parameter estimation, adaptive filtering, variable threshold logic , and statistical decision theory. Over the past two decades, basic research on FDI using analytical redundancy has evolved considerably due to the emergence of powerful techniques of mathematical modeling, state estimation, and parameter identification (Patton and co-workers, 1989; Frank, 1990). However, the complexity and the prerequisites of this approach have precluded the widespread application of these techniques.
The model-based redundancy issues have been addressed in an earlier paper (Fathi and co-workers, 1992). In this current paper, the physical system, the analytical redundancy problems, and the statistical decision methods are first briefly described. The knowledge base design and issues related to integration and implementation are then discussed. A simulation result illustrating the potency of the integrated diagnostic approach is finally presented .
The knowledge- based approach to FDI, over the past decade, has received considerable attention (Tzafestas , 1989). This approach can be quite effective in tackling the problem of diagnosis. It provides the means and flexibility in gathering and organizing the various types of knowledge present in process operation. The main issues addressed in this area of research are knowledge representation and problem-solving
43
The augmented state vector, z, and the output vector, y . • )f these filters are defined as
PROCESS DESCRIPTION The process schematic shown in Figure 2 represents tl,,· plant components and flow paths selected for prototype d,·· agnostic system development. This diagram shows the condensate pump, the control valve, the deaerator level controller, the extraction steam pipe, and the deaerator and its storage tank. It does not include the low- pressure feedwater heaters; they are represented only as flow resistance between the control valve and the deaerator. The objective in this work is the analysis of problems associated with the condensate pump , the transportation lines, the deaerator, and its control system. The following gives a brief description of the tasks that the various components perform.
Filter I Filter II
zT zT
= [wv
Ceq )
= [Pp w v )
,
y
= [wv )
(1)
,
y
= [Pp )
(2)
In Filter I , WV (the mass flow rate of water leaving valve) is the state variable and C eq (the equivalent conductance) is the unknown parameter while in Filter II, Pp (the pressure of water entering valve) is the state variable and Wv is the unknown parameter. Filter I simulates a fault model corresponding to a change in conductance (e.g., fouling) and Filter II models a fault that manifests itself as a change in flow (e.g., leak, flow sensor failure).
The condensate pump imparts kinetic energy to the pumped fluid from the hotwell of the condenser which is then recovered as stored energy at a higher fluid outlet pressure. The valve is primarily used for modulating to control the flow rate through the valve for maintaining the liquid level in the deaerator storage tank . The extraction pipe is the interconnecting component between the turbine and the deaerator through which the extraction steam flows. The deaerator level controller is a three-mode proportional- integral controller with anti-reset windup.
For the extraction pipe, the augmented state vector and the output are defined as y
= [Pe )
(3)
Here, the desired state variable is the steam pressure at the outlet of the extraction pipe, Pe, and the unknown parameter to be estimated is the steam mass flow rate , We ' This filter simulates a fault model corresponding to a low or high input steam flow to the deaerator.
The deaerator subsystem is composed of a deaerator (an open feedwater heater) and its storage tank. Deaerators serve four major tasks: (1) removal of non condensable gases to prevent corrosion and scaling of boiler surfaces due to gases dissolved in the feed water, (2) heating of feedwater, (3) provide feedwater storage, and (4) are located to provide a substantial suction head on the boiler feed pumps, preventing pump cavitation by supplying a substantial net positive suction head.
In the filter considered for the deaerator, the states and outputs are defined as
(4) The specific bulk enthalpy, hd, and the deaerator pressure , Pd , are the state variables and the output mass flowrate , Wd •• " is the unknown parameter. The measured variables are the deaerator pressure, Pd , and the liquid level in the storage tank, L. This filter is designed to detect output flow sensor failure (this sensor is in the loop of the deaerator lev(·l control system). The faulty mode is indicated by the dis crepancy between the estimated and measured quantities.
The components of the power plant subsystem have been modeled through the use of MMS (Modular Modeling Sys tern) documents obtained from EPRT (Energy and Power Research Institute). The mathematical model 10r each com ponent consists of conservation of mass, conservation of ell,.rgy, fluid mechanics, and fluid properties (Fathi and coworkers, 1992).
Another detection filter based on the deaerator model was considered for the level sensor failure. In this model, enthalpy and pressure were defined as states and pressure as the only measured quantity. A linear time-invariant approximation of the model was used to study the observability properties of the state-space model. The enthalpy was found to have very poor observability behavior. This was revealed through examining both the observability matrix and the observability gramian. The alternative is to use an autoregressive moving average model for the level sensor. This is a quite attractive approach which can in general be used for sensor validation at the highest level of troubleshooting (Yung and Clarke, 1989) .
ANALYTICALLY- REDUNDANT APPROACH To date , many FDI schemes based on the mathematical process models, estimation techniques, and statistical decision algorithms have been proposed for the stochastic and deterministic dynamic systems . Most of the work considering the problem of FDl from the point of analytical redundancy proposes to use the Kalman filter in the stochastic case, and the Luenberger observer in the deterministic case. Here, we base the design of our detectors on two concepts: structural decomposition and adaptive Kalman filtering . That is, the plant structure is decomposed into units for which individual filters are designed. In general, the design of local filters can range from one incorporating most likely fault modes to one incorporating all possible ones. To cover a broader range of practical domains, the modified extended Kalman filter which is designed for the joint state and parameter es timation in systems with coupled slow and fast dynamics is considered (Fathi and co-workers, 1991) .
For the controller, the filters are designed according to
= [Of ZT = [Of ZT
= lOT) Y = lOT )
k[ ) , y kp )
,
(5)
The integrator output, 0[, is the state variable. The unknown parameters are considered to be the integrator gain , k[, and the proportional gain, kp • The measured quantity is the controller output, OT, which is the sum of proportional and integrator outputs.
I n this section, the failure detection of the condensatefeed water subsystem of a coal-fired power plant is considered based on the analytical red undency approach. To design the local detection filters , the system shown in Figure 2 is decomposed into: condensate pump, control valve, extraction pipe, deaerator , and controller. In the following , the individual filters are briefly described . Further details on estimation algorithm and filter formulation can be found in another paper (Fathi and co-workers, 1992).
STATISTICAL DECISION METHODS Several statistical tests for the on-line detection of shifts in measurable output signals, signal trends, and characteristic quantities are considered (Basseville and Benveniste, 1983). Under normal operating conditions, the observations follow some known evolutionary patterns within certain limits of uncertainty introduced by random process disturbances and measurement noise . When faults occur, these variables deviate from their nominal trajectories. The signal derivative also permits a certain prediction of the signal progression and thus could lead to an earlier fault detection. Testing nonfiltered signal derivatives has poor performance due t o
For the deaerator level control valve (and the piping associdted with the low-pressure heaters), two filters are designr>r:I.
44
the noisy nature of the signals. However, if the signal is first filtered through a window of certain length, the statistical tests can be applied on the derivative of the filtered signal. Furthermore, the customary checking of the characteristic quantities provides essential information when supervising large process plants. These quantities , which are determined from input- output data , can also be used for detection of faults.
Our methodology is based on the use of analytical redundancy approach to FDI in the context of a knowledge base. The advantages that this integrated approach offers can be viewed from two points. First, the analytically-redundant a.pproach • is fundamentally a diagnostic technique , • can facilitate the task of diagnosis by providing additional sources of information, • allows natural use of hierarchical representation methods due to flexibility in the level of description (a marked advantage for dealing with large and complex domains), and • makes knowledge-based systems more reliable by allowing systematic isolation of faults.
A commonly used technique for the on- line detection of shifts is limit checking. One scheme, without counter, releases an alarm or warning as soon as the signal falls outside a certain tolerance of the normal value. The other schemes, with counters, detect when the number of limit crossings or the crossings of the same direction, during a time duration, exceed a certain number. A more elaborate method than limit checking is the cumulative sum procedure. It depends on the past history of the process and thus can detect a relatively small change in the process mean.
Second, in comparison with the well-known methods of functional redundancy (Patton and co- workers, 1989) that require the on-line implementation of a series of estimators based on different mathematical models and measurement subspaces, our approach provides a more effective and efficient way of employing the filter modules through the use of compiled knowledge. In other words , when an abnormality appears, a guided situation-specific filter is activated instead of a bank of filters being implemented (a computationally inefficient approach). Also since the filter is activated only when a potential problem exists, it operates in its most sensitive state of dynamic operability.
A statistical procedure used for the detection of changes due to unmodeled phenomena is also considered. When the knowledge base inquires for information from the estimation modules , a situation-specific filter is implemented. However , the validity of the suggested filter should be examined through statistical methods. A variety of statistical tests can be performed on the innovations or resid uals to determine the validity of the model used in the filter design . To monitor the measurement innovations (or residuals) , we use the modified Sequential Probability Ratio Test (Chien and Adams , 1976).
Knowledge Structure The overall objective in developing a knowledge-based system is to capture both the domain knowledge and the problem-solving strategy. Thus, an essential element in knowledge- based system development lies in understanding the problem in terms of knowledge, organization of knowledge, and reasoning strategies. Implementation of these organizational and reasoning strategies requires additional problemsolving control that has also important implications on the degree of modularity for maintaining and upgrading the knowledge- base (Davis and Gandikota, 1990).
KNOWLEDGE- BASED APPROACH Knowledge- based approaches are strongly oriented to problems of fault diagnosis. In process plant operations, a significant layer of human problem-solving method is recognized to be a knowledge- rich domain in terms of human expertise. The knowledge- based system techniques allow the symbolic reasoning of problem- solving experts to be incorporated into automated systems.
Rasmussen (1985) has discussed the role of hierarchical knowledge representation in decision making. From the controller's point of view, the complexity of a system is not an objective feature of the system and depends upon the resolution applied for information search . The control of complex systems (large number of information sources and devices) can be coped with by structuring the situation and thereby transforming the problem to a level with less resolution. From analysis of verbal protocols, Rasmussen observed this structuring in two dimensions: whole/part dimension reflecting various levels of physical decomposition and abstract / concrete dimension reflecting various levels of functional abstraction (highest level representing ultimate purpose of the system and lowest level representing physical form of the system). The role of abstraction at proper levels is to reduce the observed complexity of the system under consideration. In the space of whole/ part and abstract / concrete dimensions, the human diagnostic reasoning moves from a high level, where functional purpose of the whole system is considered, to a detailed level, where the physical form and function of the individual components are analyzed. These human organizational strategies and conceptual abstractions suggest a hierarchical representation of knowledge. The hierarchical structure offers efficient topdown solution procedures for classification-type problems.
There are some issues in regard to the previous work that need to be mentioned. Early applications of symbolic reasoning to process fault diagnosis centered around symptombased pattern recognition methods to FDI. The difficulties inherent in this approach were recognized to be lack of flexibility and reliability under novel conditions. Later work attempted to use some form of model which captures and organizes deep-process knowledge to predict system's behavior. A number of modeling methods associated with the use of knowledge- based reasoning systems may be classified as:
• Constraint- based models: Rules are derived from constraints imposed on process behavior by the physical laws (e .g., method of governing equations). • Logic-based models: This approach is generally in the form of a geometric struct ure (information flow graphs) which relates parts of the process and defines paths of causality for events occurring in the process (e.g., fault trees , signed directed graphs). • Qualitative physics: This method infers the system's behavior from knowledge of its structure (states and physical laws governing their changes). Unconstrained model- based reasoning was found to be computationally inefficient. To remedy this problem, compiled problem-solving components were integrated into deep knowledge approaches. More recently, the work in this area is directed toward blending partially-quantitative knowledge and qualitative reasoning in the framework of knowledge bases in order to overcome some of the shortcomings of the qualitative approaches (ambiguities and spurious solu lions). In spite of the many advances in diagnostic problemsolving, there still exist need and potential for further re-
An appropriate domain concept for context grouping of diagnostic knowledge in a hierarchical structure is malfunction hypothesis (Chandrasekaran, 1984, 1986). Thus, to provide a necessary general structure for the knowledge base, a malfunction hierarchy is identified for organizing knowledge about the system (Shum and co-workers, 1988). The use of malfunction hypothesis as the conceptual basis for constructing the hierarchy provides an efficient problemsolving framework , allows for a natural decomposition of the
"~arch.
45
licient, integrated medium for our knowledge- based system development.
domain for focus and control, and supports organized and efficient knowledge-acquisition. In addition, this approach provides a flexible structure for including and efficiently focusing the quantitative considerations of analytical redundancy techniques.
In our knowledge- based system, the domain is modeled in terms of objects, classes , and properties. The specific properties of objects and classes form the slots. Slots are used to store any information that comes into the knowledge- based application. For the condensate-feed water system, a knowledge base that defines the entities of the system and their characteristics in terms of classes, objects, and properties has been created. Figure 4 represents a small portion of the object network. This network has been defined using engineering knowledge on primitive concepts of the domain.
The construction of the malfunction hierarchy involves the identification of functional systems / subsystems in the process. Figure 3 represents the malfunction hierarchy generated using the system knowledge for the condensate-feedwater subsystem of a power plant. Higher levels are related to more general functional segments and physical subsystems and lower levels are related to more specific functional units and physical components. The hierarchical structure allows the decision process to proceed from a high level of generality to a high level of detail.
To represent reasoning over the object network and thus capture the knowledge necessary to solve the diagnostic problem , we use rules . For the condensate-feed water system, a knowledge base that contains the problem-solving knowledge in terms of rules has been created. This knowledge base has been developed through preliminary knowledge acquisition based on background knowledge and use of documen ts such as A nalog/ Digital Data Point and Instrumentation Logic Diagrams provided by Public Service Company, and detailed knowledge acquisition based on meetings with plant experts. Since the hierarchical malfunctiondriven approach exploits the concept grouping used by experts, the knowledge acquisition was substantially facilitated.
The diagnostic knowledge employed at the higher nodes of the hierarchy (more general malfunction hypotheses) is compiled while that at the lower nodes (more specific malfunct.ion hypotheses) makes use of estimation-based techniques . In this manner , initially the compiled knowledge is used to rapidly narrow down the solution space and provide diagnostic focus. Then, the filter modules are triggered to resolve and refine the suspected malfunction hypotheses in the reduced and more focused search space. This integrated approach increases the completeness and reliability of the diagnostic system.
To illuminate the following discussion , a few basic concepts of the knowledge processing are described. The mechanism by which events are processed is a dynamic, priority-based scheduling system (agenda). There are several types of inference search mechanisms such as backward chaining, hypothesis forward , semantic gates, context links, etc. with different priorities. Moreover, the inference strategies can be disabled or limited in scope globally or locally.
Qualitative / compiled knowledge captures directly the efficiency of domain experts in generating plausible hypotheses and serves to focus diagnostic attention on a small set of subsystems. Estimation-based methods that resort to fun damental analysis provide the rationale for a qualitativelyguided reasoning process. Reasoning with filter models in explaining transient behavior under faulty conditions allows the set of qualitatively- generated hypotheses to be resolved :lnd refined.
To modularize the knowledge base and offer more flexibility, the compiled knowledge used to obtain a diagnostic focus is weakly linked to the rules activating the quantitative analysis via context links (the lowest level event on the agenda). The weak link allows the quantitative analysis to be performed only after obtaining a focus of attention, triggered independently, or activated through other knowledge sources.
The domain knowledge used at higher nodes of the hierarchy is compiled and qualitative. This knowledge, which is obtained from domain experts, are organized high-level abstractions derived from detailed models or explicit causal reasoning, and / or developed through experience. The fact that the compiled knowledge used in the knowledge base has been derived from expert diagnosticians does not say anything about the origin of the knowledge (associationally, experientially, or causal-based; Chandrasekaran, 1991).
To provide a graphics-based user interface, MODEL and Nexpert Object are run under Microsoft Windows. The two media are interfaced through the Dynamic Data Exchange (DDE) calls of Microsoft Windows. Figure 5 depicts the software environments for various system components and how they are interfaced. In the current version of the prototype detection and diagnosis system, the inputs are simulated . These inputs are statistically tested to determine the plant condition. In case of detecting an abnormal behavior, the transformed qualitative quantities are sent to the knowledge base. Then, the top-level hypothesis in the compiled knowledge island is suggested and the knowledge processing is activated. When a diagnostic focus is obtained, the hypothesis of the rule activating a respective filter is put on the agenda. The first condition in this rule turns on the flag that triggers the estimation module. The next condition that checks the adequacy of the fault model needs the object slot whose value is determined via a statistical test in the filter module. A method is attached to the slot of this object that causes the inference to be suspended until it is resumed again by the client through the DDE calls (methods can be attached to the object slots to obtain a needed value or perform an action if the value in the slot changes). While the knowledge processing is suspended, the filter is run using a window of past and current data including last data transfer (data are saved in a history file) . The object slots containing the filter status and result are then sent through DDE calls to the knowledge base and the inference is resumed. The estimation results are tested, filter activati on flag is turned off, and finally conclusions are displayed
IMPLEMENTATION AL ENVIRONMENTS An ideal support base for developing computer- aided process engineering software would meet the requirements of a modular software development style, a uniform environment, and powerful graphics tools. Object- oriented programming lends itself well to these needs (Stephanopoulos , 1987). The development of our diagnostic system, which requires support from a multitude of generic and specific software facilities , is based upon this programming paradigm. The medium for performing numerical processing tasks such as simulation, statistical analysis, and estimation modules has been identified as MODEL (Model Software) . MODEL is a high-level, object- oriented engineering language that provides solutions for real-time distributed process simulation and control. Task characteristics naturally influence the choice of a tool for knowledge base development. The key is to choose a tool that has the feat ures suggested by the needs of problem and application. However, some of the most recent tools incorporate a multitude of knowledge representations and control techniques and are made to be appropriate for more types of problems. Nexpert Object (Neuron Data), a C- based, object- oriented, hybrid tool with a knowledge representation paradigm based on rules and objects constitutes an d·
46
An Example
niques to tasks. In W. Reitman (Ed.). Artificial Intelligence in Business, Ablex Publishing. Chandrasekaran, B. (1986). Generic tasks in knowledgebased reasoning: High-level building blocks for expert system design . IEEE Expert 1(3), 23-30. Chandrasekaran , B. (1991). Models versus rules, deep ver sus compiled , content versus form-some distinctions in knowledge systems research. IEEE Expert 6(2) 75-79. Chien, T. T. and M. B. Adams (1976). A sequential failure detection technique and its application. IEEE Trans . on Automatic Control AC-21, 750-757. Davis, J. F. and M. S. Gandikota (1990). Rule-based Systems in Chemical Engineering. In G. Stephanopoulos and J . F . Davis (Ed .). CACHE monograph series , CACHE, Austin, Texas. Fathi, Z., W. F. Ramirez, and O. Aarna (1991). A joint state and parameter estimator for systems with coupled fast and slow dynamic modes. AIChE Annual Meeting, Los Angeles, California, November 17-22. Fathi , Z., J. Korbicz, W . F. Ramirez, and G. Gilliland (1992). An integrated diagnostic system using analytical redundancy and artificial intelligence for coalfired power plants . IFAC Symposium on Control of Power Plant Systems, Munich, Germany, March 912. Frank, P. M. (1990). Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy. A survey and some new results. A utomatica 26(3), 459-474. Himmelblau, D. M. (1978). Fault Detection and Diagnosis in Chemical and Petrocl!tmicd Processes. Chemical Engineering Monograph 8, Elsevier Scientific, Amsterdam. Kramer, M. A. (1987). Malfunction diagnosis using quantitative models with non - Boolean reasoning in expert systems. MChE J. 33(1), 130- 140. Kumamoto, H., K. Ikenchi, and K. Inoue (1984). Application of expert system techniques to fault diagnosis. Chemical Engineel'ing Journal (Biochem. Eng. J.) 29(1), 1-9. Model Software. Solutions for Real Time Simulation and Control. 52 Curtis Court, Broomfield, CO 80020 . Naidu, S. R., E. Zafiriou, and T. J. McAvoy (1990). Use of neural networks for sensor failure detection in a control system. IEEE Control Systems Magazine 10(3), 49-55. Neuron Data. 444 High Street, Palo Alto, California 9430l. Patton, R., P. Frank, and R. Clark (1989). Fault Diagnosis in Dynamic Systems. Theory and Applications. Prentice Hall, New York. Rasmussen, J. (1985). The role of hierarchical knowledge representation in decisionmaking and system management. IEEE Transactions on Systems, Man, and Cybernetics SMC-15(2), 234-243. Rich, S. H. and V. Venkatasubramanian (1987). Modelbased reasoning in diagnostic expert system for chemical process plants. Computers €3 Chemical Engineering 11, 111-122. Shum, S. K ., J. F. Davis, W. F. Punch Ill, and B. Chandrasekaran (1988). An expert system approach to malfunction diagnosis in chemical plants. Computers €3 Chemical Engineering 12(1), 27-36. Stephanopoulos, G. (1987). The future of expert systems in chemical engineering. Chemical Engineering Progress 83(9), 44- 5l. Tzafestas , S. G . (Ed.) (1989). Knowledge-Based Sys tem Diagnosis, Supervision and Control. Plenum, New York. Venkatasubramanian, V . and S. H. Rich (1988). An objectoriented two- tier architecture for integrating compiled and deep-level knowledge for process diagnosis . Computers €3 Chemical Engineering 12(9), 903-92l. Yung, S. K., and D. W. Clarke (1989). Local sensor validation. Measurements and Control 22, 132-14l.
In order to illustrate the system trail and its efficacy in performing a diagnostic task, an example is presented. In this example, a fault, namely, fouling in the control valve/ condensate line is simulated . The details of the diagnosis are described below. As mentioned previously, in this phase of the project , inputs to the system are generated through simulation. The data are scanned every 10 seconds. To monitor the process , various statistical tests are performed. After some time has elapsed, the pipe conductance is reduced by 20 percent. As this disturbance is introduced, an abnormality is detected and the knowledge base is triggered . After establishing the top level hypothesis (i.e., the condensate feedwater subsystem malfunction), the pump , the level control system, and the deaerator are considered. Note that the more general rules are applied at the class level through pattern matching. The deaerator malfunction is then established and is further refined to investigating the condensate line malfunction. When this diagnostic focus is obtained, through context links the hypotheses of the rules activating the two respective filters (simulating faults in flow and conductance) are put on the agenda. The relevant filter corresponds to a fault that manifests itself as a change in conductance. The first condition simply turns on the filter flag which in turn causes the filter to be activated. A method is attached to the status slot of this object that suspends the knowledge processing until it is resumed again after filter completion. The estimation result is shown in Figure 6. The equivalent conductance (pipe and valve) is a function of valve position. As the flow resistance is increased, the valve opens up in order to prevent the deaerator level from dropping . The expected conductance represents the normal value while the estimated conductance represents the present condition. The fault is determined by examining the normalized deviation between the expected and present values. The final conclusion is then displayed as a process schematic with a warning message. This example illustrates how the analytical redundancy and the knowledge-based systems technique complement one another in providing a coherent and efficient system. This integrated approach increases the reliability and completeness of the diagnostic system.
CONCLUSION A diagnostic methodology in which the symbolic reasoning of knowledge-based systems techniques is integrated with quantitative analysis of analytical redundancy methods has been presented . The analytical algorithms are housed at the low levels of abstraction of a hierarchical knowledge structure. The knowledge at the higher levels is compiled and suggest a situation-specific filter to be employed for fault diagnostic resolution. Such an integrated approach (analytical and knowledge- based redundancy) reduces the computational complexity of the analytical algorithms and increases the effectiveness of the knowledge-based system. The numerical experiments implemented using a subsystem of a power plant illustrated the potency of the integrated approach . Further research is required in considering more elaborate techniques of analytical redundancy and actual system testing.
REFERENCES Basseville, M. and A. Benveniste (1983) . Design and comparative study of some sequential jump detection algorithms for digital systems. IEEE Trans. on Acoustics, Speech, and Signal Processing ASSP-31(3), 521-534. Chandrasekaran, B. (1984). Expert systems: Matching tech-
47
Fault Detector
Knowledge-Based System
Fig. 4.
Overall block diagram of fault detection and diagnosis system
Fig. 1.
A small portion of the overall object network for the condensate- feed water subsystem Dynamic OaU Exchange - send data • suggest top level hypothesIS - start the i{OQ',vledge processing
~
~ --r
.-1
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I I I I
l
-
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Dynamic Data Exchange • send data • start the knowldge processing
Fig. 2.
Condensate- feed water subsystem
Fig. 5.
System interface
4.0 10' , - - - - - - - - - - - - - - - - - - - , 1.00 MeCha.",cal Pump
( Elec trical level Control Valve
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E
Mechanical - - ( Recirculation Valve
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.0
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~ Upscale Transducer
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~.
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- - Estimated Flow
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2.0 1040'----1'--0--2'--0--3'--0--4'--0--5'--0----'600.50
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Fig. 6.
Malfunction hierarchy for condensate-feed water subsystem
48
~
Measured and estimated flow, expected and estimated conductance, and valve position versus time- valve module