Publication
Fault diagnosis based on causal computations
Journal Article (2012)
Journal
IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans
Pages
371-381
Volume
42
Number
2
Doc link
http://dx.doi.org/10.1109/TSMCA.2011.2164063
File
Authors
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Rosich Oliva, Albert
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Frisk, Erik
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Åslund, Jan
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Sarrate Estruch, Ramon
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Nejjari Akhi-Elarab, Fatiha
Projects associated
Abstract
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.
Categories
automation.
Author keywords
causal computations, fault diagnosis, fuel cell stack (FCS) system, sensor placement
Scientific reference
A. Rosich, E. Frisk, J. Åslund, R. Sarrate and F. Nejjari. Fault diagnosis based on causal computations. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 42(2): 371-381, 2012.
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