This book is a product of a workshop on Fault Diagnosis and Fault Tolerance for Dynamic Systems, held in Vancouver in October 2002. Each lecturer has expanded his contribution to form a chapter of the current volume. This book therefore represents a fairly up-to-date snapshot of the control engineer’s approach to fault diagnosis via state-space models. The book comprises six substantial articles by experts in the field. Although the discussion is fairly mathematical in places, there are many application examples. These are not restricted to robotic systems, but cover thermofluid systems, underwater vehicles, and aircraft. The first chapter, by Saif and Xiong, adopts an observer-based approach to the fault detection, isolation, and accommodation (FDIA) problem. In this approach, the observer is used to generate estimation errors or residuals, so that the residuals can bemonitored for large excursions,which signal deviations from the base model and hence faults. The article deals with both sensor and actuator faults. To be specific, the authors use a sliding mode observer (SMO). The SMO used is a generalization of that devised by Walcott and Zak (WZ). The WZ observer is exact for a certain class of non-linear uncertain systems, but implies strong structural constraints, and therefore, is of limited application. The new designs proposed in the article reduce the structural constraints. The first extension proposed is the sliding mode output observer (SMOO), which proves to be good for detecting large faults, but less effective at detecting incipient faults. This leads the authors to propose further extensions: the sliding mode functional observer (SMFO) and the sliding mode output functional observer (SMOFO). The methods are illustrated on an application to engine diagnostics. The second chapter by Wang, considers the problem of fault diagnosis and fault tolerant control for non-Gaussian stochastic systems with random parameters. The approach is to detect changes in the probability density function (PDF) of the parameters. Previously, this was accomplished by monitoring the mean and variance, an approach that is inadequate for non-Gaussian systems. A recursive estimator for the random parameters is derived using the measured PDF, and faults are signalled by unexpected changes in the parameter vector. An approach to fault diagnosis via the minimization of residual entropy is proposed and discussed. The application example is a refining system in a paper making process. The editors Caccavale and Villani present the third chapter on fault diagnosis for industrial robots. This is concerned with observer-based FDI again and concentrates on applications to robot manipulators. The diagnostics are designed in discrete-time to avoid problems with discretization. A simple model-based FDI scheme is first discussed and found to have drawbacks. This leads to observerbased FDI schemes. The approach is to achieve a sharp diagnosis by minimizing uncertainties. The uncertainties are dealt with by using three methods. The first is a robust time-delayed compensation scheme, the second – which proves better – is a recursive estimator, and the last is an adaptive approach that assumes a parametric model of the uncertainties. The three approaches are illustrated and compared on a six degree-of-freedom robot with both abrupt and incipient simulated faults. The next article, by Antonelli, is concerned with autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). A specific problem for the diagnosis and control of such systems is that they operate in an unstructured hostile environment. Undetected failures can lead to the loss of vehicles. Fault tolerance is discussed in the specific context of thruster redundancy. The article gives a summary of experienced failures. Both fault detection and fault tolerance are discussed and past experiments are reviewed. Chapter 5, by Boskovic and Mehra, is concerned with failure detection, identification, and reconfiguration (FDIR) in flight control. The particular challenges of FDIR design for aircraft, where a single 1015

Fault Diagnosis and Fault Tolerance for Mechatronic Systems: Recent Advances:

This paper presents a sliding mode observer (SMO) for robustly reconstructing faults affecting a class of nonlinear non-infinitely observable descriptor systems. Preliminary transformations are utilised to re-express the system such that the design freedom in its structure is easier to exploit. An infinitely observable system is formed by treating some states as unknown inputs. The faults are then reconstructed from measurable inputs and outputs using a SMO. The structure of the SMO bounds the nonlinear components of its estimation errors. The effect of disturbances on the fault reconstruction is minimised using linear matrix inequality techniques. A summarised design procedure for the SMO scheme is presented. Finally, a simulated example is carried out to showcase the efficacy of the scheme.

A sliding mode observer for robust fault reconstruction in a class of nonlinear non-infinitely observable descriptor systems

The problem of functional diagnosis of technical systems described by linear dynamic models under disturbances is considered. For solving the problems of detection, isolation and identification of faults, a method based on sliding mode observers is used. Modifications of this method for relaxing the implementation constraints in comparison with the well-known results and also for reducing the complexity of diagnostic tools are proposed.

Diagnosis of Linear Dynamic Systems: An Approach Based on Sliding Mode Observers

In this paper, we consider the problem of the functional diagnosis of technical systems described by linear differential equations of state with constant coefficients in the presence of disturbances. Sliding mode observers are used to solve problems of detecting, isolating, and identifying faults. The proposed approach is based on the idea of constructing a reduced (having a smaller dimension) model of the original system with the selective sensitivity to faults and disturbances, based on which a sliding mode observer is built. Depending on the specific properties of the constructed model, three options (in each of which a diagnostic problem is solved): accurate fault identification, fault detection, and approximate fault identification are considered. Apart from reducing the complexity of diagnostic tools, the proposed approach allows reducing the limitations imposed on the original system for constructing sliding mode observers.

Diagnosis of Linear Systems Based on Sliding Mode Observers

The problem of fault accommodation in discrete-event systems described by finite automaton models is considered. Solution to the problem is related to constructing the control law which provides full decoupling with respect to fault effects. Existing conditions are formulated and calculating relations are given for the control law determination.

Fault accommodation in systems described by finite automaton models

The problem of fault diagnosis in technical systems described by linear dynamic models is considered. The approach allowing increase robustness of the diagnostic process under disturbances is sugge...

Fault diagnosis in linear systems via sliding mode observers

Sliding Mode Observers for Fault Detection in Linear Dynamic Systems

Consideration was given to adaptation of the nonlinear dynamic systems to faults. The problem of accommodation by generating a control law enabling complete decoupling from the fault-generated actions was solved within this framework. The proposed approach was obtained in continuation of its earlier counterpart and for a wider class of systems enabled solution of the problem of accommodation and established a simpler—static, in particular,—solution.

Accommodation to defects in the discrete dynamic systems

Consideration was given to the problem of functional diagnosis of the nonlinear dynamic systems by the nonparametric method distinguished for the fact that the values of some parameters of the diagnosed system need not to be known. An approach was proposed enabling fault isolation. Established were the necessary and sufficient conditions for system transformed into the canonical form required for application of the nonparametric method at diagnosis of the given nonlinear system.

https://link.springer.com/content/pdf/10.1134%2FS0005117919020024.pdf

Alexey E. Shumsky

Papers

Fault diagnosis in linear systems via sliding mode observers

Diagnosis of Linear Dynamic Systems: An Approach Based on Sliding Mode Observers

Sliding Mode Observers for Fault Detection in Linear Dynamic Systems

Accommodation to defects in the discrete dynamic systems

Nonparametric Method for Diagnosis of Nonlinear Dynamic Systems