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Abstract |
Residual Generation for Fault Diagnosis
The objective when supervising technical processes is to alarm an
operator when a fault is detected and also identify one, or possibly a
set of components, that may have been the cause of the alarm.
Diagnosis is an expansive subject, partly due to the fact that
nowadays, more applications have more embedded computing power and
more available sensors than before. A fundamental part of many model-based diagnosis algorithms are so
called residuals. A residual is a signal that reacts to a carefully
chosen subset of the considered faults and by generating a suitable
set of such residuals, fault detection and isolation can be achieved. A common thread is the development of systematic design and analysis
methods for residual generators based on a number of different model
classes, namely deterministic and stochastic linear models on
state-space, descriptor, or transfer function form, and non-linear
polynomial systems. In addition, it is considered important that there
exist readily available computer tools for all design algorithms. A key result is the minimal polynomial basis algorithm that is used to
parameterize all possible residual generators for linear model
descriptions. It also, explicitly, finds those solutions of minimal
order. The design process and its numerical properties are shown to be
sound. The algorithms and its principles are extended to descriptor
systems, stochastic systems, nonlinear polynomial systems, and
uncertain linear systems. New results from these extensions include:
increased robustness by introduction of a reference model, a new type
of whitening filters for residual generation for stochastic systems
both on state-space form and descriptor form, and means to handle
algorithmic complexity for the non-linear design problem.
In conclusion, for the four classes of models studied, new methods
have been developed. The methods fulfills requirements generation of
all possible solutions, availability of computational tools, and
numerical soundness. The methods also provide the diagnosis system
designer with a set of tools with well specified and intuitive design
freedom.
Erik Frisk
2001
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Last updated: 2021-11-10
