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Abstract |
Model Based Fault Diagnosis: Methods, Theory, and Automotive Engine Applications
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
\emph{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 \emph{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 \emph{strong} fault detectability, are
given.
A new design method, the \emph{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.
Mattias Nyberg
1999
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Senast uppdaterad: 2021-11-10
