Robust Residual Generation Research Articles

Survey of robust residual generation and evaluation methods in observer-based fault detection systems

The paper outlines recent advances of the theory of observer-based fault diagnosis in dynamic systems towards the design of robust techniques of residual generation and residual evaluation. Emphasis will be placed upon the latest contributions using frequency domain techniques including H∞ theory, nonlinear unknown input observer theory, adaptive observer theory, artificial intelligence including fuzzy logic, knowledge-based techniques and the natural intelligence of the human operator. Two representative examples illustrate the efficiency of the observer-based approach.

Robust H ∞ /μ Observer-Based Residual Generation for Fault Diagnosis

This paper presents a new approach to the design of a robust observer-based residual generation scheme for incipient fault diagnosis. This H ∞ /μ based approach takes account of the disturbance robustness and fault sensitivity simultaneously. Unlike the robust H ∞ /μ estimator which minimises the disturbance effect on the estimation error and subsequently on the diagnostic residual, this new approach maximises the fault effect on the residual by the proper selection of the performance bound and the estimation weighting matrix of the H ∞ robust estimator. Depending on the class of modelling uncertainty considered, one or two Riccati equations are required for the H ∞ estimator design. The H ∞ estimator can be designed to be robust against; disturbance only, disturbance and parameter uncertainty only, and disturbance and a large variety of modelling errors. The μ robust estimator assumes a block diagonal structure for uncertainty and can produce a less conservative design. A 3rd order system has been used to illustrate special design features. The results show that the FDI scheme developed can effectively detect incipient faults even in the presence of disturbances and modelling errors

A Cautious Look at Robustness in Residual Generation

Abstract Various robust residual generation methods are reviewed and shown to suffer from limitations. Exact decoupling from disturbances and single parameter uncertainties is restricted by design freedom. Approximate decoupling methods are optimal in some sense but there is no guarantee as to acceptable performace. Bounded uncertainty methods lead to conservative design which may be very restrictive on fault and disturbance sizes

A Geometrical View of Observer-Based Methods for Detection and Isolation of Faults in Time Varying and Bilinear Systems

This paper addresses the problem of robust residual generation for detection and isolation of faults in linear time varying and bilinear systems by using state observers. The detection and isolation problem is discussed using algebraic methods and geometric design concepts. Along the lines of our recent results on the application of Lie algebra structures to filtering problems, it also forms an account of some of our views on designing detection filters for bilinear and, perhaps, more general classes of nonlinear plants. Emphasis is made on the comparison of the problem statements presented in the literature from this point of view and little will be said about the mathematical details of the proofs

Robust Residual Generation for Nonlinear System Fault Detection and Isolation

Abstract This paper presents the development of a fault detection and isolation scheme for nonlinear dynamic systems. The paper presents in detail the problem formulation, explores the existence conditions for fault isolation and quantifies the computational needs of the isolation scheme. A detailed procedure is presented for the construction of residual generators from models of the system to be monitored. The formulation presented here is completely-new and extremely general, being applicable to almost all kinds of faults, including input, output and component faults

Robust Residual Generation for F.D.I in Uncertain Dynamic Systems

This paper describes some new developments in the design of robust residual generator for F.D.I. The residual generator which is proposed addresses the problem of fault isolation through directional residual generation and allows to achieve some robustness performances in the frequency domain. The introduction of optimization techniques applied to models with non structured uncertainties represents the key development in the design which is proposed for robust residual generation.

On-line fault detection in uncertain nonlinear systems using diagnostic observers: a survey

The paper discusses the principles of model-based fault detection and isolation (FDI) in nonlinear and time-varying uncertain dynamic systems. Such systems are typical for such complex plants as, for example, in the chemical process industries or in advanced transportation technology. For a model-based fault diagnosis in such situations, robust or even adaptive strategies are needed. In this paper the theory of robust linear observer-based residual generation for FDI is reviewed from a general point of view. The structural equivalence between the parity space approach and observer-based approach is shown in a new simple graphical way by showing that the observer-based FDI concept can easily be transformed into an equivalent extended parity space configuration, without claiming, however, equivalence of the underlying design techniques. The unknown input observer approach known as a most powerful and comprehensive framework for robust residual generation for FDI in uncertain linear systems is extended to cl...

Robust FDI Systems and H∞-Optimization: Tall Fault Systems and Example

This paper is dedicated to the design of robust FDI systems by utilizing the H∞-optimization technique. Results are derived based on the optimization of a sensitivity (performance) index in terms of faults vs. uncertainties, with the assumption of a bounded norm for disturbances. The results show that, for a well-conditioned system, a robust residual generator exists for all possible disturbances. This paper discusses the robust design of a tall fault system (i.e. the number of faults is no greater than the number of outputs) with additive disturbances. The results are demonstrated on an illustrative example.

Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis

Methods of residual generation and evaluation for model-based fault diagnosis in uncertain linear dynamic systems are investigated with the aid of frequency domain approaches and H ∞-optimization techniques. Based on the factorization technique, the construction of residual generators in terms of a parametrization concept is proposed. With its help the fault detection and isolation problem with perfect model uncertainty decoupling is studied and, moreover, by its optimal approximation the problem of optimally robust residual generation is treated. The optimization problem is solved by H ∞-techniques. To increase the robustness of residual evaluation a frequency domain residual evaluation index is introduced, and optimal input adaptive fault thresholds are derived with respect to the frequency domain evaluation index. The results are illustrated in terms of an inverted pendulum example.

Enhancement of robustness in observer-based fault detection†

A prerequisite for the feasibility of the technique of observer-based fault detection and isolation (FDI) in dynamic systems is a satisfactory robustness with respect to modelling uncertainties. This paper surveys the most relevant methods to increase the robustness in both the stage of residual generation and residual evaluation. Among these methods are the generalized observer scheme, the robust parity space check, the unknown input and H ∞ observer scheme, the decorrelation filter, and the concept of adaptive threshold selection. It is pointed out that the unknown input observer concept, which provides perfect decoupling from the modelling errors or its optimal approximation with the aid of H ∞ techniques, constitutes a general framework of robust residual generation that generalizes and unifies numerous other approaches, among them the parity space and detection filter approach. It is further shown that this FDI method can even be applied to a certain class of nonlinear systems.

Robust Model-Based Fault Detection in Dynamic Systems

The paper discusses the design of robust fault detection and isolation (FDI) techniques using analytical models for nonlinear and time varying uncertain systems. Such systems are typical in the chemical process industries. The unknown input observer approach known as a most powerful and comprehensive framework for robust residual generation for FDI in uncertain linear systems is extended to classes of nonlinear and time varying systems. For such plants an adaptive nonlinear unknown input observer scheme is proposed. Moreover, it is demonstrated from a new graphical point of view that the observer-based approach can easily be transformed into an equivalent extended parity space configuration without claiming, however, equivalence of the resulting design techniques. Finally, residual evaluation techniques are reviewed and suggestions to increase the robustness are made.

Analytical Redundancy Methods in Fault Detection and Isolation - Survey and Synthesis

Abstract The best known residual generation methods in model based fault detection and isolation, including parity equations, diagnostic observers and Kalman filtering, are presented in a consistent framework. The discussion is organized along two residual enhancement concepts, namely structured and fixed direction residual sets. It is shown that, once the design objectives are selected, parity equation and observer based designs lead to equivalent residual generators. Robustness in the face of modelling errors is addressed and partially robust residual generator algorithms based on multiple model variants and on partial parameter insensitivity are reviewed.

A Review of Parity Space Approaches to Fault Diagnosis

Abstract This paper reviews the state of the art in fault detection and isolation for dynamic systems, based on the parity space concept. Some important definitions are provided and tutorial examples are given to illustrate the theory. The main aim has been to draw together the important links between parity space approaches; in particular the important links between open-loop and closed-loop strategies. The parity space is used as a new re-statement to unify residual generation methods. The robustness and isolation problems in fault diagnosis is mainly addressed in the paper. Moreover, an important definition for robustness is made and the recent works on robust fault diagnosis methods are described. Some new design methods are given to illustrate the potential of robust residual generation using closed-loop parity space ideas.

Enhancement of Robustness in Observer-Based Fault Detection

A prerequisite for the feasibility of the technique of observer-based fault detection and isolation (FDI) in dynamic systems is a satisfactory robustness with respect to modeling uncertainties. This paper surveys the most relevant methods to increase the robustness in both the stage of residual generation and residual evaluation. Among them are the generalized observer scheme, the robust parity space check, the unknown input and observer scheme, the decorrelation filter, and the concept of adaptive threshold selection. It is pointed out that the unknown input and H∞ observer concept that provides perfect decoupling from the modeling errors, or its optimal approximation constitutes a general framework of robust residual generation that generalizes and unifies numerous other approaches, among them the parity space and detection filter approach. It is further shown that this FDI method can even be applied to a certain class of nonlinear systems.

Sensor Fault Detection in Dynamic Systems Using Robust Observers

This paper outlines the theory of a design approach to the detection of faults in dynamic systems using the concept of the robust unknown input observer. The method which is based on eigenstructure assignment makes use of invariance concepts applicable to uncertain systems. The paper provides an interpretation of the process of robust residual generation together with the underlying principles and concepts. The eigenstructure assignment approach makes use of the full design freedom that is available in a multivariable observer system. A careful choice of the observer left eigenvectors enables a high degree of parity space de-coupling to be achieved, giving rise to an efficient fault monitor which is capable of detecting and isolating sensor faults. A discrete time design example is given which is based on a flight control system. Graphical results demonstrate the disturbance de-coupling property of the fault monitor

Analytical redundancy and the design of robust failure detection systems

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.