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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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