LiU startsida
Abstract |
Methods for Automated Design of Fault Detection and Isolation Systems with Automotive Applications
Fault detection and isolation (FDI) is essential for dependability of
complex technical systems. One important application area is
automotive systems, where precise and robust FDI is necessary in order
to maintain low exhaust emissions, high vehicle up-time, high vehicle
safety, and efficient repair. To achieve good performance, and at the
same time minimize the need for expensive redundant hardware,
model-based FDI is necessary. A model-based FDI-system typically
comprises fault detection by means of residual generation and residual
evaluation, and finally fault isolation. The overall objective of this thesis is to develop generic and
theoretically sound methods for design of model-based FDI-systems. The
developed methods are aimed at supporting an automated design
methodology. To this end, the methods require a minimum of human
interaction. By means of an automated design methodology the overall
design process becomes more efficient and systematic, which also
contributes to higher quality. These aspects are of particular
importance in an industrial context. Design of a model-based FDI-system for a complex real-world system is
an intricate task that poses several difficulties and challenges that
must be handled by the involved design methods. For instance, modeling
of these systems often result in large-scale, non-linear,
differential-algebraic models. Furthermore, despite substantial
modeling work, models are typically not able to capture the behaviors
of systems in all operating modes. This results in model-errors of
time-varying nature and magnitude. This thesis develops a set of
methods able to handle these issues in a systematic manner. Two methods for model-based residual generation are developed. The two
methods handle different stages of the design of residual
generators. The first method considers the actual residual generator
realization by means of sequential residual generation with mixed
causality. The second method considers the problem of how to select an
optimal set of residual generators from all possible residual
generators that can be created with the first method. Together the two
methods enable systematic design of a set of residual generators that
fulfills a stated fault isolation requirement. Moreover, the methods
are applicable to complex, large-scale, and non-linear
differential-algebraic models. Furthermore, a data-driven method for statistical residual evaluation
is developed. The method relies on a comparison of the probability
distributions of residuals and exploits no-fault data from the system
in order to learn the behavior of no-fault residuals. The method can
be used to design residual evaluators capable of handling residuals
subject to stochastic uncertainties and disturbances caused by for
instance time-varying model errors. The developed methods, as well as the potential of an automated design
methodology, are evaluated through extensive application studies. To
verify their generality, the methods are applied to different
automotive systems, as well as a wind turbine system. The performances
of the obtained FDI-systems are good in relation to the required
engineering effort. Particularly, no specific adaption or no tuning of
the methods, or the design methodology, were made.
Carl Svärd
2012
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Last updated: 2021-11-10
