R.J. Patton

Observer-based fault detection and isolation: Robustness and applications

Abstract This paper studies the observer-based fault detection and isolation problem with an emphasis on robustness and applications. After introducing some basic definitions, the problem of model-based fault detection and isolation is introduced. This is followed by a summary of the basic ideas behind the use of observers in generating diagnostic residual signals. The robustness issues are then defined and ideas for improving the robustness properties are outlined. This provides an opportunity to give some of the fundamental ideas behind eigenstructure assignment and the unknown input observer approaches for enhancing robustness. An important focus is on the use of disturbance principles for robust fault diagnosis. The goal of the paper is to show how robustness techniques must be used for real applications.

Soft Computing Approaches to Fault Diagnosis for Dynamic Systems: A Survey

Abstract Recent approaches to fault detection and isolation for dynamic systems using methods of integrating quantitative and qualitative model information, based upon soft computing (SC) methods are surveyed. In this study, the use of SC methods is considered an important extension to the quantitative model-based approach for residual generation in FDI. When quantitative models are not readily available, a correctly trained neural network (NN) can be used as a non-linear dynamic model of the system. However, the neural network does not easily provide insight into model behaviour; the model is explicit rather than implicit in form. This main difficulty can be overcome using qualitative modelling or rule-based inference methods. For example, fuzzy logic can be used together with state space models or neural networks to enhance FDI diagnostic reasoning capabilities. The paper discusses the properties of several methods of combining quantitative and qualitative system information and their practical value for fault diagnosis of real process systems.

An LMI approach to fault-tolerant control of uncertain systems

In this paper, the robust fault-tolerant control problem is formulated in an linear matrix inequality (LMI) setting, in which satisfactory performance and guarantees stability robustness are introduced. To achieve both performance and stability robustness, a multiobjective approach is used to establish a matrix inequality formulation for fault-tolerant control system design which is based on the assumption that both fault effect factors and uncertainties can be of linear-fractional-transformation parameter dependence. An example of flight control system is used to demonstrates the design technique presented in this paper.

Fuzzy observers for nonlinear dynamic systems fault diagnosis

This paper presents a new fault diagnosis scheme for nonlinear dynamic systems. In this scheme, the residual signal is generated by a fuzzy observer which is based on Takagi-Sugeno fuzzy models. The stability as well as eigenvalue constraint conditions for the fuzzy observer design are presented and solved in the linear matrix inequality framework. Finally, the paper demonstrates the application of fuzzy observers in detecting and isolating intermittent faults in the induction motor of a railway traction system.

Sensor fault-tolerant control of a rail traction drive

Abstract The problem of sensor faults on an AC-drive system for an electric train is considered here. Intermittent disconnections of these sensors produce severe transient errors in the estimator for the control loop. This paper uses a bilinear model of the motors and model-based techniques to produce estimates of control variables that are tolerant to intermittent disconnections, without degrading performance. The paper shows how such a system can be verified in hardware, on a small test-rig with a DSP used to run the fault-tolerant algorithm.

Active fault-tolerant flight control systems design using the linear matrix inequality method

This paper discusses the issues of robust control law design for fault-tolerant systems. Based on the assumption that the effects of faults can be expressed in linear-fractional-transformation (LFT) forms, a fault-tolerant control systems design problem is formulated and solved via a linear matrix inequality (LMI)-based synthesis approach. In order to recover the convexity of the design problem whilst considering the robust performance and robust stability against faults and uncertainties simultaneously, a constrained optimisation approach is used. The simulation results of a design example (a longitudinal motion flight control problem for an unmanned aircraft in the case of suffering battle damage on its wing ) show that robust stability and satisfactory performance have been achieved.

Fault detection for a diesel engine actuator - A benchmark for FDI

Abstract An electro-mechanical position servo is introduced as a benchmark for mode-based Fault Detection and Identification (FDI). The purpose is to provide a simple, industrial system as a platform for comparison of model-based FDI methods, and for the gathering of design experience. Despite a simple system structure, FDI design is intricate when realistic obstacles are included: measurement noise. FDI performance specifications are provided. They include requirements for detection probability and time to detect. Two mathematical models are given: a simple model for use during design, and a complex, nonlinear one for simulation and verification.

Robust fault detection of dynamic systems via genetic algorithms

Abstract This paper presents a new approach to the design of robust fault detection systems via a genetic algorithm. To achieve robustness, a number of performance indices are introduced, which are expressed in the frequency domain to account for the frequency distributions of incipient faults, noise and modelling uncertainty. All objectives are then reformulated into a set of inequality constraints on performance indices. A genetic algorithm is thus used to search an optimal solution to satisfy these inequality constraints. The approach developed is applied to a flight control system example and results show that incipient sensor faults can be detected reliably in the presence of modelling uncertainty.

Observer Design for a Class of Non-Linear Systems

Abstract In this paper we propose an observer for a class of non-linear systems. We consider the stability of the observer where the non-linearities are bounded. This paper gives also the link between the bound of the modelling errors and the dynamic of the observer.

Industrial actuator benchmark for fault detection and isolation

Abstract Feedback control systems are vulnerable to faults within the control loop, because feedback actions may cause abrupt responses and process damage when faults occur. Such faults can be detected by model-based methods for fault detection and isolation (FDI) but research results have not been widely accepted in industry. One reason has been a scarcity of realistic examples for testing FDI methods against industrial systems. These special section papers focus on a common benchmark example, an electro-mechanical position servo, used in speed control of large diesel engines. The result is a platform for comparison of FDI methods and a gathering together of design experience on a simple, yet very realistic, industrial example. This paper introduces the benchmark problem, overviews the FDI methods used within the papers and discusses the results.