Model Based Fault Diagnosis : Methods, Theory, and Automotive Engine Applications

Abstract: Model based fault diagnosis is to perform fault diagnosis by means of models. An important question is how to use the models to construct a diagnosis system. To develop a general theory for this, useful in real applications, is the topic of the first part of this thesis. The second part deals with design of linear residual generators and fault detectability analysis.A general framework, for describing and analyzing diagnosis problems, is proposed. Within this framework a diagnosis method structured hypothesis tests is developed. It is based on general hypothesis testing and the task of diagnosis is transferred to the task of validating a set of different models with respect to the measured data. The procedure of deriving the diagnosis statement, i.e. the output from the diagnosis system, is in this method formalized and described by logic.Arbitrary types of faults, including multiple faults, can be handled, both in the general framework and also in the method structured hypothesis tests. It is shown how well known methods for fault diagnosis fit into the general framework. Common methods such as residual generation, parameter estimation, and statistically based methods can be seen as different methods to generate test quantities within the method structured hypothesis tests.Based on the general framework, a method for evaluating and comparing diagnosis systems is developed. Concepts from decision theory and statistics are used to define a performance measure, which reflects the probability of e.g. false alarm and missed detection. Based on the evaluation method, a procedure for automatic design of diagnosis systems is developed.Within the framework, diagnosis systems for the air-intake system of automotive engines are designed. In one case, the procedure for automatic design is used. Also the methods for evaluation of diagnosis systems are applied. The whole design chain is described, including the modeling of the engine. All diagnosis systems are validated in experiments using data from a real engine. This application highlights the strengths of the method structured hypothesis tests, since a large variety of different faults need to be diagnosed. To the authors knowledge, the same problem can not be solved using previous methods.In the second part of the thesis, linear residual generation is investigated by using a notion of polynomial bases for residual generators. It is shown that the order of such a basis doesn't need to be larger than the system order. Fault detectability, seen as a system property, is investigated. New criterions for fault detectability, and especially strong fault detectability, are given.A new design method, the minimal polynomial basis approach, is presented. This method is capable of generating all residual generators, explicitly those of minimal order. Since the method is based on established theory for polynomial matrices, standard numerically efficient design tools are available. Also, the link to the well known Chow-Willsky scheme is investigated. It is concluded that in its original version, it has not the nice properties of the minimal polynomial basis approach.

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