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Journal ArticleDOI

Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—a survey and some new results

01 May 1990-Automatica (Pergamon)-Vol. 26, Iss: 3, pp 459-474

Abstract: The paper reviews the state of the art of fault detection and isolation in automatic processes using analytical redundancy, and presents some new results. It outlines the principles and most important techniques of model-based residual generation using parameter identification and state estimation methods with emphasis upon the latest attempts to achieve robustness with respect to modelling errors. A solution to the fundamental problem of robust fault detection, providing the maximum achievable robustness by decoupling the effects of faults from each other and from the effects of modelling errors, is given. This approach not only completes the theory but is also of great importance for practical applications. For the case where the prerequisites for complete decoupling are not given, two approximate solutions—one in the time domain and one in the frequency domain—are presented, and the crossconnections to earlier approaches are evidenced. The resulting observer schemes for robust instrument fault detection, component fault detection, and actuator fault detection are briefly discussed. Finally, the basic scheme of fault diagnosis using a combination of analytical and knowledge-based redundancy is outlined.
Citations
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Journal ArticleDOI
Youmin Zhang1, Jin Jiang2Institutions (2)
TL;DR: A bibliographical review on reconfigurable fault-tolerant control systems (FTCS) is presented, with emphasis on the reconfiguring/restructurable controller design techniques.
Abstract: In this paper, a bibliographical review on reconfigurable (active) fault-tolerant control systems (FTCS) is presented. The existing approaches to fault detection and diagnosis (FDD) and fault-tolerant control (FTC) in a general framework of active fault-tolerant control systems (AFTCS) are considered and classified according to different criteria such as design methodologies and applications. A comparison of different approaches is briefly carried out. Focuses in the field on the current research are also addressed with emphasis on the practical application of the techniques. In total, 376 references in the open literature, dating back to 1971, are compiled to provide an overall picture of historical, current, and future developments in this area.

2,259 citations


Journal ArticleDOI
TL;DR: This three part series of papers is to provide a systematic and comparative study of various diagnostic methods from different perspectives and broadly classify fault diagnosis methods into three general categories and review them in three parts.
Abstract: Fault detection and diagnosis is an important problem in process engineering It is the central component of abnormal event management (AEM) which has attracted a lot of attention recently AEM deals with the timely detection, diagnosis and correction of abnormal conditions of faults in a process Early detection and diagnosis of process faults while the plant is still operating in a controllable region can help avoid abnormal event progression and reduce productivity loss Since the petrochemical industries lose an estimated 20 billion dollars every year, they have rated AEM as their number one problem that needs to be solved Hence, there is considerable interest in this field now from industrial practitioners as well as academic researchers, as opposed to a decade or so ago There is an abundance of literature on process fault diagnosis ranging from analytical methods to artificial intelligence and statistical approaches From a modelling perspective, there are methods that require accurate process models, semi-quantitative models, or qualitative models At the other end of the spectrum, there are methods that do not assume any form of model information and rely only on historic process data In addition, given the process knowledge, there are different search techniques that can be applied to perform diagnosis Such a collection of bewildering array of methodologies and alternatives often poses a difficult challenge to any aspirant who is not a specialist in these techniques Some of these ideas seem so far apart from one another that a non-expert researcher or practitioner is often left wondering about the suitability of a method for his or her diagnostic situation While there have been some excellent reviews in this field in the past, they often focused on a particular branch, such as analytical models, of this broad discipline The basic aim of this three part series of papers is to provide a systematic and comparative study of various diagnostic methods from different perspectives We broadly classify fault diagnosis methods into three general categories and review them in three parts They are quantitative model-based methods, qualitative model-based methods, and process history based methods In the first part of the series, the problem of fault diagnosis is introduced and approaches based on quantitative models are reviewed In the remaining two parts, methods based on qualitative models and process history data are reviewed Furthermore, these disparate methods will be compared and evaluated based on a common set of criteria introduced in the first part of the series We conclude the series with a discussion on the relationship of fault diagnosis to other process operations and on emerging trends such as hybrid blackboard-based frameworks for fault diagnosis

2,144 citations


Journal ArticleDOI
TL;DR: This final part discusses fault diagnosis methods that are based on historic process knowledge that need to be addressed for the successful design and implementation of practical intelligent supervisory control systems for the process industries.
Abstract: In this final part, we discuss fault diagnosis methods that are based on historic process knowledge. We also compare and evaluate the various methodologies reviewed in this series in terms of the set of desirable characteristics we proposed in Part I. This comparative study reveals the relative strengths and weaknesses of the different approaches. One realizes that no single method has all the desirable features one would like a diagnostic system to possess. It is our view that some of these methods can complement one another resulting in better diagnostic systems. Integrating these complementary features is one way to develop hybrid systems that could overcome the limitations of individual solution strategies. The important role of fault diagnosis in the broader context of process operations is also outlined. We also discuss the technical challenges in research and development that need to be addressed for the successful design and implementation of practical intelligent supervisory control systems for the process industries.

1,771 citations


Journal ArticleDOI
TL;DR: The approach to failure diagnosis presented in this paper is applicable to systems that fall naturally in the class of DES's; moreover, for the purpose of diagnosis, most continuous variable dynamic systems can be viewed as DES's at a higher level of abstraction.
Abstract: Fault detection and isolation is a crucial and challenging task in the automatic control of large complex systems We propose a discrete-event system (DES) approach to the problem of failure diagnosis We introduce two related notions of diagnosability of DES's in the framework of formal languages and compare diagnosability with the related notions of observability and invertibility We present a systematic procedure for detection and isolation of failure events using diagnosers and provide necessary and sufficient conditions for a language to be diagnosable The diagnoser performs diagnostics using online observations of the system behavior; it is also used to state and verify off-line the necessary and sufficient conditions for diagnosability These conditions are stated on the diagnoser or variations thereof The approach to failure diagnosis presented in this paper is applicable to systems that fall naturally in the class of DES's; moreover, for the purpose of diagnosis, most continuous variable dynamic systems can be viewed as DES's at a higher level of abstraction >

1,494 citations


Journal ArticleDOI
Zhiwei Gao1, Carlo Cecati2, Steven X. Ding3Institutions (3)
TL;DR: The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade.
Abstract: With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

1,398 citations


References
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Journal ArticleDOI
Alan S. Willsky1Institutions (1)
01 Nov 1976-Automatica
TL;DR: This paper surveys a number of methods for the detection of abrupt changes in stochastic dynamical systems, focusing on the class of linear systems, but the basic concepts carry over to other classes of systems.
Abstract: In this paper we survey a number of methods for the detection of abrupt changes (such as failures) in stochastic dynamical systems. We concentrate on the class of linear systems, but the basic concepts, if not the detailed analyses, carry over to other classes of systems. The methods surveyed range from the design of specific failure-sensitive filters, to the use of statistical tests on filter innovations, to the development of jump process formulations. Tradeoffs in complexity vs performance are discussed.

2,378 citations


Journal ArticleDOI
01 Jul 1984-Automatica
TL;DR: This contribution presents a brief summary of some basic fault detection methods, followed by a description of suitable parameter estimation methods for continuous-time models.
Abstract: The supervision of technical processes is the subject of increased development because of the increasing demands on reliability and safety. The use of process computers and microcomputers permits the application of methods which result in an earlier detection of process faults than is possible by conventional limit and trend checks. With the aid of process models, estimation and decision methods it is possible to also monitor nonmeasurable variables like process states, process parameters and characteristic quantities. This contribution presents a brief summary of some basic fault detection methods. This is followed by a description of suitable parameter estimation methods for continuous-time models. Then two examples are considered, the fault detection of an electrical driven centrifugal pump by parameter monitoring and the leak detection for pipelines by a special correlation method.

2,323 citations


01 Jan 1975-
Abstract: A number of methods for detecting abrupt changes (such as failures) in stochastic dynamical systems are surveyed. The class of linear systems is concentrated on but the basic concepts, if not the detailed analyses, carry over to other classes of systems. The methods surveyed range from the design of specific failure-sensitive filters, to the use of statistical tests on filter innovations, to the development of jump process formulations. Tradeoffs in complexity versus performance are discussed.

1,451 citations


Journal ArticleDOI
E. Chow, Alan S. Willsky1Institutions (1)
Abstract: The failure detection and identification (FDI) process is viewed as consisting of two stages: residual generation and decision making. It is argued that a robust FDI system can be achieved by designing a robust residual generation process. Analytical redundancy, the basis for residual generation, is characterized in terms of a parity space. Using the concept of parity relations, residuals can be generated in a number of ways and the design of a robust residual generation process can be formulated as a minimax optimization problem. An example is included to illustrate this design methodology.

1,440 citations


Book
01 Nov 1989-

1,162 citations


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
20221
202144
202061
201978
201873
201797