- Email: [email protected]
Fault Detection in Industrial Processes
Copyright © IFAC Information Control in Manufacturing, Nancy - Metz, France, 1998
FAULT DETECTION IN INDUSTRIAL PROCESSES
Paul M. Frank
Gerhard-Mercator-Universitat-GH Duisburg Fachbereich 9, Mef3- u. Regelungstechnik, Bismarckstr. 81 (BB), D-47048 Duisburg, Germany e-mail: [email protected]
Abstract : This paper is intended to set the line for a discussion of how. to incr~e ~ult tolerance of complex industrial processes via model-based fault detection and IsolatIon (FDI). The state of the art of model-based FDI using analytical ~d .knowledge~based models is reviewed along with a brief outline of both the basIc Ideas of different approaches and some perspectives for future research and development in this field. Copyright © 1998 IFAC Keywords: Fault Detection, Process Supervision, Model-Based Fault Detection and Isolation, Fuzzy Logic, Neural Networks, Industrial Processes
Fault detection has thus become an important issue in industrial processes, and during the last 25 years an immense deal of research and development was done in this field, resulting in a great variety of different techniques with increasing acceptance in practice.
1. INTRODUCTION Due to the increasing complexity of au tomatic processes and the growing demands for quality, cost efficiency, availability, reliability and safety, the call for fault tolerance in connection with control is gaining more and more importance. Fault tolerance can be achieved either by passive or by active strategies. The passive approach makes use of robust control techniques to ensure that the process becomes insensitive with respect to faults. In contrast, the active approach provides fault accommodation, i.e., the reconfiguration of the control system after a fault has occ.ured. Whilst robust control can tolerate small faults to a certain degree, the reconfiguration concept is absolutely indispensable when substantial faults occur that would lead to a failure of the whole system.
The core of the FDI methodology is the so-called model-based approach where either analytical or knowledge-b~ed models or combinations of them are used together with analytical or heuristic reasoning, respectively. The model-based approach profits decisively from both the advances in modern control theory - especially in the fields of modeling, identification, observer theory, pattern recognition, artificial intelligence - and the immense progress in computer technology. In return it stimulates new research in a certain direction of modern control theory. This paper briefly reviews the state of the art summarising the various approaches to model-based fault diagnosis, and outlines some perspectives. For a closer study of the methods and techniques and for more literature the reader is referred to comprehensive survey papers as, for example, by Gertler (1991) , Frank (1990) , Frank (1996) , Isermann (1993) ~nd PattoD (1993) or the books by Patton, Frank and Clark (1989) and (1997) .
In order to accomplish fault accommodation, a number of tasks have to be performed, one of the most important and difficult ones is the early detection and isolation (FDI) of the faults. Besides this, fault detection is needed for the supervision of complex processes that incorporate modern methods of artificial intelligenc.e (e. g. fuzzy or neural network control), where no analytical guarantees for stability and robustness can be given.
891
2. PROBLEM STATEMENT
w--..~---.
The problem of model-based fault detection can be stated as follows: Given a plant of an automatic control system with the known input vector u and the output vector y as depicted in fig. 1.
• decision function generator • fault decision logic
Known
Alarms
Residual evaluation (decision making)
faults f
Control output
inputs u
unknown inputs (parameter variations, disturbances, noise)
y
Fig. 1. Definition of faults in the plant of the process.
Suppose there may occur faults in the functional devices of the plant that lead to undesired or intolerable performance (failure) of the control system.
Fig. 2. The two-step concept of model-based fault detec-
tion and isolation (FDI) 3. STATE OF THE ART
The goal of fault detection is to detect and localize the faults early enough so that approprjate steps can be undertaken in order to avoid a failing of the overall system. In addition to the faults there is always modeling uncertainty due to unmodeled disturbances, noise and model mismatch. The latter may not be critical for the process behaviour but may obscure the fault detection by producing false alarms. The modeling uncertainty can be taken into consideration by the vector of so-called unknown inputs.
Typical for the model-based approach to FDI is that the actual situation of the system under consideration is compared to that of its nominal fault-free model. The dynamic behaviour of a system can be described either by a quantitative (analytical) model or a qualitative model that makes use of the knowledge about the system in terms of rules and facts with due regard of the human operator's observations. Unfortunately, precise analytical models are in practice hardly (in complex systems almost never) available, in which case qualitative models can be seen as the only realistic alternative allowing to exploit as much knowledge about the process as available. The existing methods of model-based fault detection are therefore divided into two major categories: analytical model-based and knowledge-based concepts. The resulting three different strategies to implement model-based concepts are illustrated in fig. 3.
The task of fault detection and isolation (FDI) consists detecting and localizing occuring faults in the devices of the process under supervision. In order to configurate the system before an undesired failure of the whole system occurs, the FDI has to be done on-line in the face of the unknown inputs without (or with a minimum of) false alarms. The FDI proceure consists of the following two steps: 1. Residual (symptoln) generation, i. e. the gen-
eration of signals symptoms which reflect the faults. In order to localize the faults within the system, properly structured residuals or directed residual vectors are needed.
3.1. ANALYTICAL MODEL-BASED APPROACHES
2. Residual evaluation (fault classification), i. e. logical decision-making on the time of occurrence and the location of a fault.
Most of the work in model-based FDI focused on analytical residual generation. The underlying idea is to compare measured signals y of the actual system with those of a nominal mathematical model which represents the fault-free dynamic behaviour of the system and is driven by the same known inputs, u.
The overall structural diagram of the model-based residual generation and evaluation system is illustrated in fig. 2.
There are many ways to exploit the analytical redundancy given by the mathematical model. The numerous methods that have been developed during the last two
892
ANAl.Y1lCAL MODEL ~SED
I / [······.--.oiGnAi··········· [····-·······EXPERT············ :
COMPUTER
Unknown inputs d
KNOWlEDGE MODEl BASED
:
SYSTEM
Input
~
HUMAN EXPERT
COMPUlER + ANALYTICAl SIGNAl PROCESSING
COMPUTER + HEURIsnCAl KNO'NlEDGE PROCESSING
NATURAL
I
I
I
u
.
HUMAN TOOLS
( i1~ )_ , .......
I
N.JALYTICAl DIAGNOSIS SYSTEM
"
OBSERVER; KALMAN-FILTER
~----'
Fig. 4. Basic configuration of an observer-based residual
,....-----.;;;---, . . :.' ....------=-----.
generator.
OPERATOR + COMPUTER
compensate for a mismatch of the initial conditions and for stabilisation of the observer in the case of unstable systems. It also provides design freedom to reach fault isolation and robustness, in the ideal sense to decouple the effects of faults from each other to reach fault isolation, and/or to decouple the effects of faults from the effects of unknown inputs to reach robustness. Note that robustness is the key issue for the practical applicability of any fault detection system.
Fig. 3. Strategies of implementation of model-based FDI
and a half decades can be divided into three categories: • parity space approach • diagnostic observer approach • parameter estimation approach The parity space approach, introduced in the early eighties, is based on the check of parity equations with measured signals from the actual process ("parity check") (Patton 1993 ). The parity equations are properly modified system equations of the fault-free case. The faults are detected and isolated by proper evaluation of the inconsistency of these equations. The modification aims at decoupling between the effects of the faults for the purpose of fault isolation. The parity space approach in its original form leads to a special class of observers called dead-beat observers. However, it is possible to extend the parity space approach so that more general observers are obtained, which means that in principle both approaches lead to similar detection filter architectures with only different design algorithms.
In a similar way one can use reduced-order observers, Kalman filters (in the case of noisy measurements) or nonlinear observers (in the case of nonlinear processes) (Frank 1990). The residual or a function (or functional) of it has to be designed such that a given threshold is surpassed if a fault occurs. For fault isolation and for robust fault detection structured residuals or directed (perfectly or approximately decoupled) residual vectors are required. For decoupling one uses mostly banks of observers (" observer schemes") where the individual observers are tuned for the different faults of interest. Many such observer schemes have been proposed over the years and some of them, e.g., the dedicated observer scheme Patton, Frank and Clark (1989) or the generalised observer scheme (Frank 1990 , Frank 1996 ), are well established and successfully working in real practical applications. Currently, some research is focused on adaptive and nonlinear diagnostic observer schemes with significant robustness properties. In this connection neural network are becoming increasingly important.
The idea of the observer-based approach consists of the reconstruction of the outputs of the process with the aid of observers or Kalman filters and to use the estimation error (or innovation, respectively) or a function of it as residual. Note that contrary to the state observer that is needed for control in the case of incomplete measurement of the state vector, a diagnostic observer is an output observer.
Among the appealing properties of the observer-based approach is that the observer configuration can also be used - with some modifications - as the basic architecture for residual generation using knowledge-based models ("knowledge observer", Frank (1996) ) or databased models. Much research work is currently being done in this field with some interesting results with qual-
The standard scheme of a diagnostic observer (of full order) is shown in fig. 4. The actual output vector y is compared with the output vector iJ of the nominal model and the difference r = y - fJ is fed back wi th the feedback gain matrix H. The feedback is necessary to
893
itative fuzzy or neural network modeling. However, the knowledge observer concept is still far away from being mature enough for practical applications.
Operator
i
I
!
f
Human-
Com~uter
The parameter estimation approach is based on the assumption that the faults of the functional devices of a system reflect themselves in the system parameters such as friction, mass, viscosity, inductance etc.. The basic idea of the detection method is that the parameters of the actual system are repeatedly estimated on-line using the parameter estimation methods and the results are compared to the parameters of the reference model obtained initially under fault-free conditions (Patton, Frank and Clark 1989). Any substantial discrepancy indicates a change in the system and may be interpreted as a fault. Besides a well established theory, there exists a rich experience in the practical application of this method to industrial processes (Isermann 199:3 , Patton, Frank and Clark 1989 ).
Monitored Process data
I
1
Knowledge Acquisition ~
Inteiface
Analytical
..
!
I
Heuristic
1
Knowledge Processing Residual Generation
!
I Extraction Feature
-
1
Inference Engine
fault infonnation
Threshold logic Hypothesis test Perfonnance index Pattern recognition Decision making
i
The parameter-estimation-based approach has long been considered as a basically different approach to the observer-based approach. However, recent investigations show that there is a close relationship between both concepts and that under certain conditions the one can be transformed into the other by a nonlinear transformation (for more literature see Frank 1996). Nevertheless, the parameter estimation allows commonly a deeper insight into the process and is therefore more useful for fault analysis whereas the power of diagnostic observers lies in a quick fault detection and isolation. Therefore, both concepts are complementary and should be combined in practical applications.
i
Knowledge base Facts I Rules Process models Nominal behaviour Fault tree Fault statistics Process environment Expert's experience
~
update
Fig. 5. Basic architecture of a knowledge-model-based
fault detection system. evaluation, is a complex process of decision making that transforms quantitative knowledge into qualitative ("yes-no" etc. statements). It can also be seen as a classification problem, where each pattern of the residual (or symptom) vector has to match one of pre-assigned classes of faults or of the fault-free case. Thus, residual evaluation is a typical field of application of artificial intelligence. A great variety of well established decision making algorithms exist including threshold tests, statistical methods (e.g. likelihood test), pattern recognition. Currently, much research is done in the application of fuzzy logic and neural network theory with very encouraging results.
3.2. KNOWLEDGE-BASED APPROACH In the case of fault diagnosis in complex systems one is faced with the problem that the system is often nonlinear and no or no sufficiently accurate mathematical models are available. The use of knowledge-ulodelbased techniques either in the framework of diagnosis expert systems or in combination with a human expert (see fig 3) is then the only feasible way. There is a number different methods of how to configure and implement a knowledge-model-based fault detection system and many proposals have been made (see Patton, Frank and Clark 1989 ). A most powerful concept currently widely discussed in the literature is to combine knowledge-based methods with analytical methods of modeling and decision making in an expert system environment (Frank 1990 , Patton 1993 ). The basic architecture is depicted in fig. 5.
3.3. THE FUZZY LOGIC APPROACH There are several reasons for the application of fuzzy logic in fault diagnosis concepts of complex systems (Kiupel and Frank (1993)). Firstly, for residual generation: Because of the lack of accurate mathematical models,
The second step in fault detection, the residual
894
qualitative modeling using fuzzy s~ts is a logical and most useful means to utilize incomplete knowledge of the dynamics of the system and to take the uncertainty into account. Secondly, for residual evaluation: Since the residuals are corrupted by the effects of modeling uncertainty, thresholds larger than zero are needed to avoid false alarms which implies a reduction of fault detection sensitivity; such threshold uncertainty can well be handled by fuzzy logic (Frank 1996 ). Thirdly, for monitoring: The monitoring of faults in terms of linguistic variables is more human oriented and therefore better tailored for the human operator.
(Himmelblau, Braker and Suewatanakul 1991 , Sorsa and Koivo 1993 , Tzafestas and Dalianis 1994 ). Artificial neural networks consist of neurons, simple processing elements, which are activated as soon as their inputs exceed certain thresholds. The neurons are arranged in layers which are connected such that the signals at the input are propagated through the network to the output. The choice of the transfer characteristic of each neuron (e.g. sigmoidal function) contributes to the nonlinear overall behaviour of the network. During a training process a set of parameters of the network is learned which leads to the "best" approximation of the desired behaviour. If a neural net is used for fault detection, the training is performed with measurements (data) from the fault-free and, if possible, the faulty plant.
The fuzzy residual evaluation procedure consists of the following three steps: 1. Fuzzification. A suitable number of fuzzy sets is assigned to each residual component ri.
2. Inference. In the next step each combination of the residuals has to be considered in the IN F ERENCE algorithm. That means, that one has to assign to each combination of residuals a fault event using a fuzzy conditional statement
= rl1)AND TH EN (cause = fl) IF (effect
IF (effect
Residuals
= r12) ...
Residual Generation (1) Fig. 6. Application of a neural network to residual gen-
where fm denotes the mth fault of the system. With this kind of statement it is now possible to attribute for each fault f an effect r responsible for this fault. The fuzzy conditional statenlent transforms the fault knowledge of the process, which can, for example, be represented by a fault tree, into the fuzzy inference engine. Each rule has the form IF effect r THEN cause =
f.
eration
For residual generation the neural network replaces the analytical model that describes the process under normal operation, fig. 6 (Koppen-Seliger and Frank 1995 ). The neural net has to be trained first for both the nominal and faulty situations. For the training, an input data base and a corresponding output signal data base are needed.
=
3. Monitoring of the faults. The final task is the display of the evaluated fault symptoms to the operator. Here, the use of fuzzy logic opens an appealing alternative to the conventional concepts. By abstaining from a defuzzification the fuzzy information can directly be made accessible to the human operator either in a linguistic format or in terms of fuzzy sets (e.g. in a gray tone range). This allows the human operator to make the final decision by including direct process observations, empirical and statistical knowledge along with human common sense.
In order to apply neural networks for residual evaluation, the network is fed with residuals which can either be generated by another neural network as or by one of the analytical methods described earlier. Before applying the neural network for residual evaluation, the network has to be trained using a residual data base and a corresponding fault signature data base as illustrated in fig. 7. After finishing the training, the neural network can be used for on-line residual evaluation to decide whether and where a fault has occured. Recent investigations show that good results can be achieved by using neural networks for both residual generation and residual evaluation (Koppen-Seliger 1997 , Koppen-Seliger and Frank 1995 , Tzafestas and Dalinais 1994 ).
3.4. THE NEURAL NETWORK APPROACH
Since the late eighties artificial neural networks have been widely studied for fault detection in complex systems where analytical models are not or hardly available
895
To summarise, with the results obtained by robust analytical model-based fault detection schemes and the advances by using knowledge-based methods in recent years, there is now a broad methodological basis for fault tolerant control systems.
5. REFERENCES
~~~(ll~rj~~'fiI~tiQiiI~-::]!m8@[fI~::=;=:r--+
@J
....:.:'».:,.,:.--.--+----i.
@ ~
..
~
..
,' ;'._,;;
~
'
~
~'
,
'.,
•..•....•... .'
'
;
Frank (1990). Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy - A survey and some new results. A utomatica, Vol. 26, 459-474. Frank (1996). Quantitative and qualitative model-based fault diagnosis - A survey and some new results. European Journal of Control, Vol. 2, No. 1. Gertler (1991). Analytical redundancy methods in fault detection and isolation. Proc. of the IFAC/IMACS
. ,'~--.® ,
,
.
Fig. 7. Training and on-line application of a neural net-
Symposium SAFEPROCESS, Baden-Baden, 9-21.
work to residual evaluation
Himmelblau, Braker and Suewatanakul (1991). Fault Classification with the aid of artificial neural networks. IFAC/IMAC-Symposion Safeprocess'91
4. CONCLUSION AND PERSPECTIVES
Baden-Baden, 369-373.
In the fi~ld of FDI there is a rapid development from the well-established but in their efficiency limited signalbased methods towards the model-based approaches using analytical and/or knowledge-based models for residual generation and modern concepts of residual evaluation making use of artificial intelligence.
Hoskins, Kaliyur and Himmelblau (1991). Fault diagnosis in complex chemical plants using artifical neural networks. AIChE J., 137-142. Isermann (1993). Fault diagnosis of machines via parameter estimation and knowledge processing. A utonlatica, Vol.29, 815-836. Kiupel and Frank (1993). Process supervision with the aid of fuzzy logic. In IEEE International Confer-
In the field of research, priority is presently given to methods that combine analytical and knowledge-based approaches with due consideration of fuzzy logic and neural networks. The application of model-based detection schemes to more complex and essentially nonlinear systems requires further extensions to nonlinear residual generation techniques and learning algorithms.
ence on Systenls, M an and Cybernetics, Le Touquet, France.
Koppen-Seliger (1997). Fehlerdiagnose mit kiinstlichen neuronalen Netzen. VDI Verlag, Diisseldorf, No. 632. Koppen-Seliger and Frank (1995). Fault Detection and Isolation in Technical Processes with Neural Networks. Conference on Decision and Control CDC'95,
As far as the practical applications are concerned, the analytical redundancy concepts are already working with success in various fields of application. In addition, there are currently many development projects in industry to design FDI systems using advanced model-based approaches.
New Orleans, U.S.A ..
Patton, Frank and Clark (1989). Fault diagnosis in dynamic systems, theory and application. Prentice Hall. Patton, Frank and Clark (1997). Advanced Fault Diagnosis for Dynamic Systems. Springer, London. Patton (1993). Robustness issues in fault-tolerant control. TOOLDIAG '93 1nl. Conf on Fault Diagnosis,
But there are also some reservations in practice. Concerning the analytical approach the crux is the need of mathematical models and still too little practical experience. Currently, there is an increasing demand for knowledge-based supervision systems, especially in process automation, because complex systems can not or at least not easily be described by analytical models. Also, the direct integration of the human operator in the fault decision process with the support of fuzzy logic in the man-machine interface is gaining practical importance, especially in the process industry.
Toulouse.
Sorsa and Koivo (1991). Application of artifical neural networks in process fault diagnosis. IFAC/IMACSynlposiunl on fault detection supervision and saftey for technical Processes Safeprocess '91, Baden-Baden.
Tzafestas and Dalianis (1994). Fault Diagnosis in Complex Systems using Artificial Neural Networks. The Third IEEE Conference on Control Applications, Glasgow, 877-882.
896
FAULT DETECTION IN INDUSTRIAL PROCESSES
Paul M. Frank
Gerhard-Mercator-Universitat-GH Duisburg Fachbereich 9, Mef3- u. Regelungstechnik, Bismarckstr. 81 (BB), D-47048 Duisburg, Germany e-mail: [email protected]
Abstract : This paper is intended to set the line for a discussion of how. to incr~e ~ult tolerance of complex industrial processes via model-based fault detection and IsolatIon (FDI). The state of the art of model-based FDI using analytical ~d .knowledge~based models is reviewed along with a brief outline of both the basIc Ideas of different approaches and some perspectives for future research and development in this field. Copyright © 1998 IFAC Keywords: Fault Detection, Process Supervision, Model-Based Fault Detection and Isolation, Fuzzy Logic, Neural Networks, Industrial Processes
Fault detection has thus become an important issue in industrial processes, and during the last 25 years an immense deal of research and development was done in this field, resulting in a great variety of different techniques with increasing acceptance in practice.
1. INTRODUCTION Due to the increasing complexity of au tomatic processes and the growing demands for quality, cost efficiency, availability, reliability and safety, the call for fault tolerance in connection with control is gaining more and more importance. Fault tolerance can be achieved either by passive or by active strategies. The passive approach makes use of robust control techniques to ensure that the process becomes insensitive with respect to faults. In contrast, the active approach provides fault accommodation, i.e., the reconfiguration of the control system after a fault has occ.ured. Whilst robust control can tolerate small faults to a certain degree, the reconfiguration concept is absolutely indispensable when substantial faults occur that would lead to a failure of the whole system.
The core of the FDI methodology is the so-called model-based approach where either analytical or knowledge-b~ed models or combinations of them are used together with analytical or heuristic reasoning, respectively. The model-based approach profits decisively from both the advances in modern control theory - especially in the fields of modeling, identification, observer theory, pattern recognition, artificial intelligence - and the immense progress in computer technology. In return it stimulates new research in a certain direction of modern control theory. This paper briefly reviews the state of the art summarising the various approaches to model-based fault diagnosis, and outlines some perspectives. For a closer study of the methods and techniques and for more literature the reader is referred to comprehensive survey papers as, for example, by Gertler (1991) , Frank (1990) , Frank (1996) , Isermann (1993) ~nd PattoD (1993) or the books by Patton, Frank and Clark (1989) and (1997) .
In order to accomplish fault accommodation, a number of tasks have to be performed, one of the most important and difficult ones is the early detection and isolation (FDI) of the faults. Besides this, fault detection is needed for the supervision of complex processes that incorporate modern methods of artificial intelligenc.e (e. g. fuzzy or neural network control), where no analytical guarantees for stability and robustness can be given.
891
2. PROBLEM STATEMENT
w--..~---.
The problem of model-based fault detection can be stated as follows: Given a plant of an automatic control system with the known input vector u and the output vector y as depicted in fig. 1.
• decision function generator • fault decision logic
Known
Alarms
Residual evaluation (decision making)
faults f
Control output
inputs u
unknown inputs (parameter variations, disturbances, noise)
y
Fig. 1. Definition of faults in the plant of the process.
Suppose there may occur faults in the functional devices of the plant that lead to undesired or intolerable performance (failure) of the control system.
Fig. 2. The two-step concept of model-based fault detec-
tion and isolation (FDI) 3. STATE OF THE ART
The goal of fault detection is to detect and localize the faults early enough so that approprjate steps can be undertaken in order to avoid a failing of the overall system. In addition to the faults there is always modeling uncertainty due to unmodeled disturbances, noise and model mismatch. The latter may not be critical for the process behaviour but may obscure the fault detection by producing false alarms. The modeling uncertainty can be taken into consideration by the vector of so-called unknown inputs.
Typical for the model-based approach to FDI is that the actual situation of the system under consideration is compared to that of its nominal fault-free model. The dynamic behaviour of a system can be described either by a quantitative (analytical) model or a qualitative model that makes use of the knowledge about the system in terms of rules and facts with due regard of the human operator's observations. Unfortunately, precise analytical models are in practice hardly (in complex systems almost never) available, in which case qualitative models can be seen as the only realistic alternative allowing to exploit as much knowledge about the process as available. The existing methods of model-based fault detection are therefore divided into two major categories: analytical model-based and knowledge-based concepts. The resulting three different strategies to implement model-based concepts are illustrated in fig. 3.
The task of fault detection and isolation (FDI) consists detecting and localizing occuring faults in the devices of the process under supervision. In order to configurate the system before an undesired failure of the whole system occurs, the FDI has to be done on-line in the face of the unknown inputs without (or with a minimum of) false alarms. The FDI proceure consists of the following two steps: 1. Residual (symptoln) generation, i. e. the gen-
eration of signals symptoms which reflect the faults. In order to localize the faults within the system, properly structured residuals or directed residual vectors are needed.
3.1. ANALYTICAL MODEL-BASED APPROACHES
2. Residual evaluation (fault classification), i. e. logical decision-making on the time of occurrence and the location of a fault.
Most of the work in model-based FDI focused on analytical residual generation. The underlying idea is to compare measured signals y of the actual system with those of a nominal mathematical model which represents the fault-free dynamic behaviour of the system and is driven by the same known inputs, u.
The overall structural diagram of the model-based residual generation and evaluation system is illustrated in fig. 2.
There are many ways to exploit the analytical redundancy given by the mathematical model. The numerous methods that have been developed during the last two
892
ANAl.Y1lCAL MODEL ~SED
I / [······.--.oiGnAi··········· [····-·······EXPERT············ :
COMPUTER
Unknown inputs d
KNOWlEDGE MODEl BASED
:
SYSTEM
Input
~
HUMAN EXPERT
COMPUlER + ANALYTICAl SIGNAl PROCESSING
COMPUTER + HEURIsnCAl KNO'NlEDGE PROCESSING
NATURAL
I
I
I
u
.
HUMAN TOOLS
( i1~ )_ , .......
I
N.JALYTICAl DIAGNOSIS SYSTEM
"
OBSERVER; KALMAN-FILTER
~----'
Fig. 4. Basic configuration of an observer-based residual
,....-----.;;;---, . . :.' ....------=-----.
generator.
OPERATOR + COMPUTER
compensate for a mismatch of the initial conditions and for stabilisation of the observer in the case of unstable systems. It also provides design freedom to reach fault isolation and robustness, in the ideal sense to decouple the effects of faults from each other to reach fault isolation, and/or to decouple the effects of faults from the effects of unknown inputs to reach robustness. Note that robustness is the key issue for the practical applicability of any fault detection system.
Fig. 3. Strategies of implementation of model-based FDI
and a half decades can be divided into three categories: • parity space approach • diagnostic observer approach • parameter estimation approach The parity space approach, introduced in the early eighties, is based on the check of parity equations with measured signals from the actual process ("parity check") (Patton 1993 ). The parity equations are properly modified system equations of the fault-free case. The faults are detected and isolated by proper evaluation of the inconsistency of these equations. The modification aims at decoupling between the effects of the faults for the purpose of fault isolation. The parity space approach in its original form leads to a special class of observers called dead-beat observers. However, it is possible to extend the parity space approach so that more general observers are obtained, which means that in principle both approaches lead to similar detection filter architectures with only different design algorithms.
In a similar way one can use reduced-order observers, Kalman filters (in the case of noisy measurements) or nonlinear observers (in the case of nonlinear processes) (Frank 1990). The residual or a function (or functional) of it has to be designed such that a given threshold is surpassed if a fault occurs. For fault isolation and for robust fault detection structured residuals or directed (perfectly or approximately decoupled) residual vectors are required. For decoupling one uses mostly banks of observers (" observer schemes") where the individual observers are tuned for the different faults of interest. Many such observer schemes have been proposed over the years and some of them, e.g., the dedicated observer scheme Patton, Frank and Clark (1989) or the generalised observer scheme (Frank 1990 , Frank 1996 ), are well established and successfully working in real practical applications. Currently, some research is focused on adaptive and nonlinear diagnostic observer schemes with significant robustness properties. In this connection neural network are becoming increasingly important.
The idea of the observer-based approach consists of the reconstruction of the outputs of the process with the aid of observers or Kalman filters and to use the estimation error (or innovation, respectively) or a function of it as residual. Note that contrary to the state observer that is needed for control in the case of incomplete measurement of the state vector, a diagnostic observer is an output observer.
Among the appealing properties of the observer-based approach is that the observer configuration can also be used - with some modifications - as the basic architecture for residual generation using knowledge-based models ("knowledge observer", Frank (1996) ) or databased models. Much research work is currently being done in this field with some interesting results with qual-
The standard scheme of a diagnostic observer (of full order) is shown in fig. 4. The actual output vector y is compared with the output vector iJ of the nominal model and the difference r = y - fJ is fed back wi th the feedback gain matrix H. The feedback is necessary to
893
itative fuzzy or neural network modeling. However, the knowledge observer concept is still far away from being mature enough for practical applications.
Operator
i
I
!
f
Human-
Com~uter
The parameter estimation approach is based on the assumption that the faults of the functional devices of a system reflect themselves in the system parameters such as friction, mass, viscosity, inductance etc.. The basic idea of the detection method is that the parameters of the actual system are repeatedly estimated on-line using the parameter estimation methods and the results are compared to the parameters of the reference model obtained initially under fault-free conditions (Patton, Frank and Clark 1989). Any substantial discrepancy indicates a change in the system and may be interpreted as a fault. Besides a well established theory, there exists a rich experience in the practical application of this method to industrial processes (Isermann 199:3 , Patton, Frank and Clark 1989 ).
Monitored Process data
I
1
Knowledge Acquisition ~
Inteiface
Analytical
..
!
I
Heuristic
1
Knowledge Processing Residual Generation
!
I Extraction Feature
-
1
Inference Engine
fault infonnation
Threshold logic Hypothesis test Perfonnance index Pattern recognition Decision making
i
The parameter-estimation-based approach has long been considered as a basically different approach to the observer-based approach. However, recent investigations show that there is a close relationship between both concepts and that under certain conditions the one can be transformed into the other by a nonlinear transformation (for more literature see Frank 1996). Nevertheless, the parameter estimation allows commonly a deeper insight into the process and is therefore more useful for fault analysis whereas the power of diagnostic observers lies in a quick fault detection and isolation. Therefore, both concepts are complementary and should be combined in practical applications.
i
Knowledge base Facts I Rules Process models Nominal behaviour Fault tree Fault statistics Process environment Expert's experience
~
update
Fig. 5. Basic architecture of a knowledge-model-based
fault detection system. evaluation, is a complex process of decision making that transforms quantitative knowledge into qualitative ("yes-no" etc. statements). It can also be seen as a classification problem, where each pattern of the residual (or symptom) vector has to match one of pre-assigned classes of faults or of the fault-free case. Thus, residual evaluation is a typical field of application of artificial intelligence. A great variety of well established decision making algorithms exist including threshold tests, statistical methods (e.g. likelihood test), pattern recognition. Currently, much research is done in the application of fuzzy logic and neural network theory with very encouraging results.
3.2. KNOWLEDGE-BASED APPROACH In the case of fault diagnosis in complex systems one is faced with the problem that the system is often nonlinear and no or no sufficiently accurate mathematical models are available. The use of knowledge-ulodelbased techniques either in the framework of diagnosis expert systems or in combination with a human expert (see fig 3) is then the only feasible way. There is a number different methods of how to configure and implement a knowledge-model-based fault detection system and many proposals have been made (see Patton, Frank and Clark 1989 ). A most powerful concept currently widely discussed in the literature is to combine knowledge-based methods with analytical methods of modeling and decision making in an expert system environment (Frank 1990 , Patton 1993 ). The basic architecture is depicted in fig. 5.
3.3. THE FUZZY LOGIC APPROACH There are several reasons for the application of fuzzy logic in fault diagnosis concepts of complex systems (Kiupel and Frank (1993)). Firstly, for residual generation: Because of the lack of accurate mathematical models,
The second step in fault detection, the residual
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qualitative modeling using fuzzy s~ts is a logical and most useful means to utilize incomplete knowledge of the dynamics of the system and to take the uncertainty into account. Secondly, for residual evaluation: Since the residuals are corrupted by the effects of modeling uncertainty, thresholds larger than zero are needed to avoid false alarms which implies a reduction of fault detection sensitivity; such threshold uncertainty can well be handled by fuzzy logic (Frank 1996 ). Thirdly, for monitoring: The monitoring of faults in terms of linguistic variables is more human oriented and therefore better tailored for the human operator.
(Himmelblau, Braker and Suewatanakul 1991 , Sorsa and Koivo 1993 , Tzafestas and Dalianis 1994 ). Artificial neural networks consist of neurons, simple processing elements, which are activated as soon as their inputs exceed certain thresholds. The neurons are arranged in layers which are connected such that the signals at the input are propagated through the network to the output. The choice of the transfer characteristic of each neuron (e.g. sigmoidal function) contributes to the nonlinear overall behaviour of the network. During a training process a set of parameters of the network is learned which leads to the "best" approximation of the desired behaviour. If a neural net is used for fault detection, the training is performed with measurements (data) from the fault-free and, if possible, the faulty plant.
The fuzzy residual evaluation procedure consists of the following three steps: 1. Fuzzification. A suitable number of fuzzy sets is assigned to each residual component ri.
2. Inference. In the next step each combination of the residuals has to be considered in the IN F ERENCE algorithm. That means, that one has to assign to each combination of residuals a fault event using a fuzzy conditional statement
= rl1)AND TH EN (cause = fl) IF (effect
IF (effect
Residuals
= r12) ...
Residual Generation (1) Fig. 6. Application of a neural network to residual gen-
where fm denotes the mth fault of the system. With this kind of statement it is now possible to attribute for each fault f an effect r responsible for this fault. The fuzzy conditional statenlent transforms the fault knowledge of the process, which can, for example, be represented by a fault tree, into the fuzzy inference engine. Each rule has the form IF effect r THEN cause =
f.
eration
For residual generation the neural network replaces the analytical model that describes the process under normal operation, fig. 6 (Koppen-Seliger and Frank 1995 ). The neural net has to be trained first for both the nominal and faulty situations. For the training, an input data base and a corresponding output signal data base are needed.
=
3. Monitoring of the faults. The final task is the display of the evaluated fault symptoms to the operator. Here, the use of fuzzy logic opens an appealing alternative to the conventional concepts. By abstaining from a defuzzification the fuzzy information can directly be made accessible to the human operator either in a linguistic format or in terms of fuzzy sets (e.g. in a gray tone range). This allows the human operator to make the final decision by including direct process observations, empirical and statistical knowledge along with human common sense.
In order to apply neural networks for residual evaluation, the network is fed with residuals which can either be generated by another neural network as or by one of the analytical methods described earlier. Before applying the neural network for residual evaluation, the network has to be trained using a residual data base and a corresponding fault signature data base as illustrated in fig. 7. After finishing the training, the neural network can be used for on-line residual evaluation to decide whether and where a fault has occured. Recent investigations show that good results can be achieved by using neural networks for both residual generation and residual evaluation (Koppen-Seliger 1997 , Koppen-Seliger and Frank 1995 , Tzafestas and Dalinais 1994 ).
3.4. THE NEURAL NETWORK APPROACH
Since the late eighties artificial neural networks have been widely studied for fault detection in complex systems where analytical models are not or hardly available
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To summarise, with the results obtained by robust analytical model-based fault detection schemes and the advances by using knowledge-based methods in recent years, there is now a broad methodological basis for fault tolerant control systems.
5. REFERENCES
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Frank (1990). Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy - A survey and some new results. A utomatica, Vol. 26, 459-474. Frank (1996). Quantitative and qualitative model-based fault diagnosis - A survey and some new results. European Journal of Control, Vol. 2, No. 1. Gertler (1991). Analytical redundancy methods in fault detection and isolation. Proc. of the IFAC/IMACS
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.
Fig. 7. Training and on-line application of a neural net-
Symposium SAFEPROCESS, Baden-Baden, 9-21.
work to residual evaluation
Himmelblau, Braker and Suewatanakul (1991). Fault Classification with the aid of artificial neural networks. IFAC/IMAC-Symposion Safeprocess'91
4. CONCLUSION AND PERSPECTIVES
Baden-Baden, 369-373.
In the fi~ld of FDI there is a rapid development from the well-established but in their efficiency limited signalbased methods towards the model-based approaches using analytical and/or knowledge-based models for residual generation and modern concepts of residual evaluation making use of artificial intelligence.
Hoskins, Kaliyur and Himmelblau (1991). Fault diagnosis in complex chemical plants using artifical neural networks. AIChE J., 137-142. Isermann (1993). Fault diagnosis of machines via parameter estimation and knowledge processing. A utonlatica, Vol.29, 815-836. Kiupel and Frank (1993). Process supervision with the aid of fuzzy logic. In IEEE International Confer-
In the field of research, priority is presently given to methods that combine analytical and knowledge-based approaches with due consideration of fuzzy logic and neural networks. The application of model-based detection schemes to more complex and essentially nonlinear systems requires further extensions to nonlinear residual generation techniques and learning algorithms.
ence on Systenls, M an and Cybernetics, Le Touquet, France.
Koppen-Seliger (1997). Fehlerdiagnose mit kiinstlichen neuronalen Netzen. VDI Verlag, Diisseldorf, No. 632. Koppen-Seliger and Frank (1995). Fault Detection and Isolation in Technical Processes with Neural Networks. Conference on Decision and Control CDC'95,
As far as the practical applications are concerned, the analytical redundancy concepts are already working with success in various fields of application. In addition, there are currently many development projects in industry to design FDI systems using advanced model-based approaches.
New Orleans, U.S.A ..
Patton, Frank and Clark (1989). Fault diagnosis in dynamic systems, theory and application. Prentice Hall. Patton, Frank and Clark (1997). Advanced Fault Diagnosis for Dynamic Systems. Springer, London. Patton (1993). Robustness issues in fault-tolerant control. TOOLDIAG '93 1nl. Conf on Fault Diagnosis,
But there are also some reservations in practice. Concerning the analytical approach the crux is the need of mathematical models and still too little practical experience. Currently, there is an increasing demand for knowledge-based supervision systems, especially in process automation, because complex systems can not or at least not easily be described by analytical models. Also, the direct integration of the human operator in the fault decision process with the support of fuzzy logic in the man-machine interface is gaining practical importance, especially in the process industry.
Toulouse.
Sorsa and Koivo (1991). Application of artifical neural networks in process fault diagnosis. IFAC/IMACSynlposiunl on fault detection supervision and saftey for technical Processes Safeprocess '91, Baden-Baden.
Tzafestas and Dalianis (1994). Fault Diagnosis in Complex Systems using Artificial Neural Networks. The Third IEEE Conference on Control Applications, Glasgow, 877-882.
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