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Abstract |
Probabalistic Fault Diagnosis with Automotive Applications
The aim of this thesis is to contribute to improved diagnosis of
automotive vehicles. The work is driven by case studies, where
problems and challenges are identified. To solve these problems,
theoretically sound and general methods are developed. The methods are
then applied to the real world systems.
To fulfill performance requirements automotive vehicles are becoming
increasingly complex products. This makes them more difficult to
diagnose. At the same time, the requirements on the diagnosis itself
are steadily increasing. Environmental legislation requires that
smaller deviations from specified operation must be detected earlier.
More accurate diagnostic methods can be used to reduce maintenance
costs and increase uptime. Improved diagnosis can also reduce safety
risks related to vehicle operation.
Fault diagnosis is the task of identifying possible faults given
current observations from the systems. To do this, the internal
relations between observations and faults must be identified. In
complex systems, such as automotive vehicles, finding these relations
is a most challenging problem due to several sources of uncertainty.
Observations from the system are often hidden in considerable levels
of noise. The systems are complicated to model both since they are
complex and since they are operated in continuously changing
surroundings. Furthermore, since faults typically are rare, and
sometimes never described, it is often difficult to get hold of enough
data to learn the relations from.
Due to the several sources of uncertainty in fault diagnosis of
automotive systems, a probabilistic approach is used, both to find the
internal relations, and to identify the faults possibly present in the
system given the current observations. To do this successfully, all
available information is integrated in the computations.
Both on-board and off-board diagnosis are considered. The two tasks
may seem different in nature: on-board diagnosis is performed without
human integration, while the off-board diagnosis is mainly based on
the interactivity with a mechanic. On the other hand, both tasks
regard the same vehicle, and information from the on-board diagnosis
system may be useful also for off-board diagnosis. The probabilistic
methods are general, and it is natural to consider both tasks.
The thesis contributes in three main areas. First, in Paper 1 and 2,
methods are developed for combining training data and expert knowledge
of different kinds to compute probabilities for faults. These methods
are primarily developed with on-board diagnosis in mind, but are also
applicable to off-board diagnosis. The methods are general, and can be
used not only in diagnosis of technical system, but also in many other
applications, including medical diagnosis and econometrics, where both
data and expert knowledge are present.
The second area concerns inference in off-board diagnosis and
troubleshooting, and the contribution consists in the methods
developed in Paper 3 and 4. The methods handle probability
computations in systems subject to external interventions, and in
particular systems that include both instantaneous and
non-instantaneous dependencies. They are based on the theory of
Bayesian networks, and include event-driven non-stationary dynamic
Bayesian networks (nsDBN) and an efficient inference algorithm for
troubleshooting based on static Bayesian networks. The framework of
nsDBN event-driven nsDBN is applicable to all kinds of problems
concerning inference under external interventions.
The third contribution area is Bayesian learning from data in the
diagnosis application. The contribution is the comparison and
evaluation of five Bayesian methods for learning in fault diagnosis in
Paper 5. The special challenges in diagnosis related to learning from
data are considered. It is shown how the five methods should be
tailored to be applicable to fault diagnosis problems.
To summarize, the five papers in the thesis have shown how several
challenges in automotive diagnosis can be handled by using
probabilistic methods. Handling such challenges with probabilistic
methods has a great potential. The probabilistic methods provide a
framework for utilizing all information available, also if it is in
different forms and. The probabilities computed can be combined with
decision theoretic methods to determine the appropriate action after
the discovery of reduced system functionality due to faults.
Anna Pernestål
2009
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Last updated: 2021-11-10
