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2013 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Model-Based Fault Diagnosis Techniques

Design Schemes, Algorithms and Tools

verfasst von: Steven X. Ding

Verlag: Springer London

Buchreihe : Advances in Industrial Control

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik"

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Über dieses Buch

Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools.

This second edition of Model-Based Fault Diagnosis Techniques contains:

• new material on fault isolation and identification and alarm management;

• extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises;

• addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and

• enhanced discussion of residual evaluation which now deals with stochastic processes.

Model-based Fault Diagnosis Techniques will interest academic researchers working in fault identification and diagnosis and as a text it is suitable for graduate students in a formal university-based course or as a self-study aid for practising engineers working with automatic control or mechatronic systems from backgrounds as diverse as chemical process and power engineering.

Inhaltsverzeichnis

Frontmatter

Introduction, basic concepts and preliminaries

Chapter 1. Introduction
Abstract
This chapter gives an overview of main traditional fault diagnosis techniques, including hardware redundancy based technique, signal processing based fault diagnosis and plausibility test, and their classification. The model-based fault diagnosis is then introduced in relationship with these traditional fault diagnosis techniques. It is followed by a review of the historical development of the model-based fault diagnosis technique and the major tasks in designing a model-based fault diagnosis system.
Steven X. Ding
Chapter 2. Basic Ideas, Major Issues and Tools in the Observer-Based FDI Framework
Abstract
Chapter 2 serves as an outline of the major issues to be addressed in this book. In the framework of the model-based fault diagnosis technique, whose core consists of residual generation, evaluation and threshold computation, unknown input decoupling, robustness in residual generation, residual evaluation and threshold computation, FDI (fault detection and isolation) system design and synthesis are the major topics which are briefly reviewed in this chapter. Also in this chapter, the major mathematical and control theoretical methods to be used as tools for solving FDI problems are briefly introduced.
Steven X. Ding
Chapter 3. Modelling of Technical Systems
Abstract
Numerous system representation forms are first presented in this chapter, which allow us to model nominal dynamic systems both in the time and frequency domains. These types of models are then extended to address disturbances (unknown inputs) and to model different types of faults. Also model uncertainties are dealt with in this chapter. As a major tool for the subsequent studies in this book, the so-called coprime factorization technique is introduced.
The second part of this chapter is dedicated to the application (case) examples and their mathematical models. They are: Speed control system of a DC motor, inverted pendulum control system, three-tank system, vehicle lateral dynamic system, and continuous stirred tank heater.
Steven X. Ding
Chapter 4. Fault Detectability, Isolability and Identifiability
Abstract
Fault detectability, isolability and identifiability describe structural properties of a dynamic system, which are of fundamental importance in dealing with the analysis and synthesis of model-based FDI systems. This chapter gives the definitions and existence conditions of fault detectability, isolability and identifiability, and together with them, some examples.
Steven X. Ding

Residual generation

Chapter 5. Basic Residual Generation Methods
Abstract
Beginning with this chapter, residual generation issues are addressed in the next four chapters. The main objective of this chapter is to introduce the basic ideas of residual generation on the one hand and to present most popular residual generation schemes, including fault detection filters (FDF), diagnostic observers (DO), parity space (PA) schemes, on the other hand.
In order to investigate the interconnections among these residual generation schemes and to study optimum and robustness issues in the sequel, remarkable attention is also paid to the parameterization of all LTI (linear time-invariant) residual generators, which can be expressed in terms of an FDF and a (dynamic) post-filter.
Steven X. Ding
Chapter 6. Perfect Unknown Input Decoupling
Abstract
“Perfect unknown input decoupling” is used to describe those residual generators, which deliver a residual signal perfectly decoupled from the unknown inputs in the process under consideration and thus are also called unknown input residual generators. In Chap. 6, the existence conditions for such kind of residual generators are first studied. It is followed by a comprehensive study on designing unknown input residual generators. The major design schemes are: UIFDF (unknown input FDF), UIDO (unknown input DO), and UIPA (unknown input PA). For the design purpose, different control theoretical and mathematical methods are applied, including eigenstructure assignment approach, geometric approach, unknown input observer theory, matrix pencil approach.
Steven X. Ding
Chapter 7. Residual Generation with Enhanced Robustness Against Unknown Inputs
Abstract
The investigation in this chapter is a logic consequence of the study in the previous chapter, where it is demonstrated that a perfect unknown input decoupling can only be achieved under the strict conditions that are often too hard for a real application. Alternatively, the well-established robust control theory can be applied to the development of residual generators being robust against unknown inputs. To this end, the needed mathematical and control theoretical preliminaries are first introduced in this chapter with a focus on the co-inner–outer factorization technique and LMI (linear matrix inequality) optimization technique.
The first scheme presented in Chap. 7 is the Kalman filter based residual generation, which is widely used in dealing with FD (fault detection) issues in stochastic processes. In the framework of PA based residual generation, the robust residual generation is addressed in the context of a trade-off between the robustness against unknown inputs and the sensitivity for the faults, which is then formulated as optimization problems with different performance indices. Numerous algorithms for the solutions of these optimization problems are derived, which lead to the design of parity vector or matrix. Analog to this study, optimal design of FDFs is also dealt with in the trade-off context. With the aid of the LMI technique, the so-called, \(\mathcal{H}_{2}/\mathcal{H}_{2}\), \(\mathcal{H}_{\infty}/\mathcal{H}_{\infty}\) and \(\mathcal{H}_{-}/\mathcal{H}_{\infty}\) and FDF design schemes are proposed. The last FDF scheme proposed in this chapter is the unified solution, which is developed on the basis of the co-inner–outer factorization. It is remarkable that the unified solution solves the above-mentioned, \(\mathcal{H}_{2}/\mathcal{H}_{2}\), \(\mathcal{H}_{\infty}/\mathcal{H}_{\infty}\) and \(\mathcal{H}_{-}/\mathcal{H}_{\infty}\) optimal design problems simultaneously. The detailed study on the unified solution gives a deep insight into the model-based residual generation, which will also play an important role in the integrated design of FD systems to be addressed in the subsequent chapters.
Steven X. Ding
Chapter 8. Residual Generation with Enhanced Robustness Against Model Uncertainties
Abstract
Chapter 8 is dedicated to the most challenging topic on the mode-based residual generation, that is, residual generation based on a model with uncertainties. To this end, preliminary knowledge of advanced LMI technique and stability of systems with stochastic uncertainties is first introduced. The core of this chapter is the reference model based residual generation strategy, in which the unified solution presented in Chap. 7 serves as the reference model delivering a reference residual signal. The residual generator design is then realized on the basis of a minimization of the difference between the reference and real residual signals. This strategy is applied to the FDF design for systems with norm-bounded uncertainties, polytopic uncertainties, and stochastic uncertainties.
Steven X. Ding

Residual evaluation and threshold computation

Chapter 9. Norm-Based Residual Evaluation and Threshold Computation
Abstract
Chapter 9 is the first chapter devoted to the residual evaluation issues. In the norm-based residual evaluation framework, signal norms are used for the evaluation of residual signals delivered by a residual generator, in order to make a right decision on the existence of a fault.
In this chapter, basic concepts and standard evaluation functions widely used in practice are first briefly reviewed. Associated with them, definitions of different types of thresholds are introduced. Using the LMI technique, computation algorithms for the threshold setting are developed, including, for systems with different types of model uncertainties. Finally, the threshold generator scheme is addressed at the end of this chapter.
Steven X. Ding
Chapter 10. Statistical Methods Based Residual Evaluation and Threshold Setting
Abstract
Statistical methods are widely used in detecting changes in signals. The objective of Chap. 11 is the application of some basic statistic methods to the evaluation of residual signals delivered by a model-based residual generator. For this purpose, elementary statistical methods are first introduced with a focus on the GLR (generalized likelihood ratio) technique. It is followed by the application of the GLR technique to residual evaluation and threshold setting in the statistical framework.
Steven X. Ding
Chapter 11. Integration of Norm-Based and Statistical Methods
Abstract
Aiming at developing residual evaluation and threshold computation schemes for processes under different environmental conditions, integration of the norm-based and the statistical methods introduced in Chaps. 9–10 is addressed in this chapter. The first integration scheme is dedicated to the residual evaluation and threshold setting issues for stochastic systems with deterministic disturbances. It is followed by the development of the second scheme for systems with stochastic uncertainties.
In the last part of this chapter, the so-called probabilistic robustness (randomized) technique is introduced and applied to the threshold setting. The major advantage of this technique is that the threshold can be set in the statistical framework and significantly less conservative than the setting in the norm-based framework.
Steven X. Ding

Fault detection, isolation and identification schemes

Chapter 12. Integrated Design of Fault Detection Systems
Abstract
After the residual generation and evaluation issues have been handled separately in the previous chapters, the objective of Chap. 12 is to approach the synthesis and analysis of model-based fault detection systems by an integrated handling of residual generation and evaluation. To this end, a norm-based framework for the evaluation of FD performance is first established whose core is the definitions of FAR (false alarm rate) and FDR (fault detection rate). In this framework, two different FD system design objectives are then formulated: maximization of FDR by a given FAR and minimization of FAR by a given FDR. For both cases, solutions are derived, in which the residual generator, evaluator and threshold setting are designed in an integrated manner. Moreover, relationships of these two schemes to the residual generation schemes presented in Chap. 7 are investigated.
Steven X. Ding
Chapter 13. Fault Isolation Schemes
Abstract
In practice, fault isolation is, in most cases, the next step to be followed after a fault is detected. One of the widely used fault isolation strategies is to generate a bank of residual signals in such a way that each of them will only be influenced by a single fault. In Chap. 13, the existence conditions for such fault isolation systems are first investigated. It is then followed by the description of three fault isolation schemes:
  • fault isolation filter, including FDF approach, the geometric approach, a generalized decoupling approach
  • PA based fault isolation and
  • residual generator bank, including DOS (the dedicated observer scheme) and GOS (the generalized observer scheme).
Steven X. Ding
Chapter 14. Fault Identification Schemes
Abstract
Fault identification (also called fault estimation) is often integrated into a fault-tolerant system and thus receiving considerable attention in the integrated design of control and fault diagnosis. The first part of this chapter is dedicated to the study on a perfect or optimal fault recovery (estimation/identification) without any assumption on faults to be estimated. The result with the existence conditions for a perfect fault identification reveals the difficulty with a successful fault identification. Also, an \(\mathcal{H}_{\infty}\) optimal design of a fault identification filter (FIF) can only be achieved on some hard conditions. For this reason, alternative fault identification schemes are proposed. They are: identification of the size of the fault (instead of the fault itself), fault estimation in a finite frequency interval, fault estimation in the PA framework, fault identification using an augmented observer, and the adaptive observer-based fault identification.
Steven X. Ding
Chapter 15. Fault Diagnosis in Feedback Control Systems and Fault-Tolerant Architecture
Abstract
In practice, model-based fault diagnosis is often integrated into a feedback control loop, in order to enhance the system reliability and availability. The study on fault diagnosis in feedback control systems is thus of special interest in many real applications. The major focus of this chapter is on the residual generation in a feedback control loop without an observer running parallel to the controller and, based on it, the realization of fault detection schemes.
The basis of the residual generation and fault detection schemes is an observer and residual generator based realization of the Youla parameterization of all stabilization controllers, which allows residual generation embedded in the control loop and building of a fault-tolerant architecture. The last part of this chapter is dedicated to the realization and implementation of residual generation and fault detection schemes in different control configurations.
Steven X. Ding
Backmatter
Metadaten
Titel
Model-Based Fault Diagnosis Techniques
verfasst von
Steven X. Ding
Copyright-Jahr
2013
Verlag
Springer London
Electronic ISBN
978-1-4471-4799-2
Print ISBN
978-1-4471-4798-5
DOI
https://doi.org/10.1007/978-1-4471-4799-2

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