Fault Diagnosis Based on Causal Computations
This paper focuses on residual generation for model-based fault
diagnosis. Specifically, a methodology to derive residual generators
when nonlinear equations are present in the model is developed. A main
result is the characterization of computation sequences that are
particularly easy to implement as residual generators and that take
causal information into account. An efficient algorithm, based on the
model structure only, which finds all such computation sequences, is
derived. Furthermore, fault detectability and isolability performances
depend on the sensor configuration. Therefore, another contribution is
an algorithm, also based on the model structure, that places sensors
with respect to the class of residual generators that take causal
information into account. The algorithms are evaluated on a complex
highly nonlinear model of a fuel cell stack system. A number of
residual generators that are, by construction, easy to implement are
computed and provide full diagnosability performance predicted by the
model.
Albert Rosich, Erik Frisk, Jan Åslund, Ramon Sarrate and Fatiha Nejjari
IEEE Transactions on Systems, Man, and Cybernetics -- Part A: Systems and Humans,
2012
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