Fault detection in dynamic systems by a Fuzzy/Bayesian network formulation. D’Angelo, M., F.; Palhares, R., M.; Cosme, L., B.; Aguiar, L., A.; Fonseca, F., S.; and Caminhas, W., M. Applied Soft Computing, 21:647-653, 8, 2014.
Fault detection in dynamic systems by a Fuzzy/Bayesian network formulation [link]Website  abstract   bibtex   
In this paper the fault detection problem is solved using an alternative methodology based on a fuzzy/Bayesian strategy combining a Bayesian network and the fuzzy set theory. The new important issue in this proposed methodology is to address uncertainties in the input of the Bayesian Network. This contribution is possible since the fuzzy set theory is used as the knowledge representation. To illustrate the technique, the fault detection problem in induction machine stator-winding is considered. Specifically, the faults in the induction machine stator-winding are detected by a state change of stator current. Simulation results are presented to illustrate the advance of the proposed methodology when compared to standard Bayesian network.
@article{
 title = {Fault detection in dynamic systems by a Fuzzy/Bayesian network formulation},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 keywords = {Bayesian networks,Fault detection,Fuzzy sets,Induction machine,Inter-turn faults},
 pages = {647-653},
 volume = {21},
 websites = {http://www.sciencedirect.com/science/article/pii/S1568494614001707},
 month = {8},
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 abstract = {In this paper the fault detection problem is solved using an alternative methodology based on a fuzzy/Bayesian strategy combining a Bayesian network and the fuzzy set theory. The new important issue in this proposed methodology is to address uncertainties in the input of the Bayesian Network. This contribution is possible since the fuzzy set theory is used as the knowledge representation. To illustrate the technique, the fault detection problem in induction machine stator-winding is considered. Specifically, the faults in the induction machine stator-winding are detected by a state change of stator current. Simulation results are presented to illustrate the advance of the proposed methodology when compared to standard Bayesian network.},
 bibtype = {article},
 author = {D’Angelo, Marcos F.S.V. and Palhares, Reinaldo M. and Cosme, Luciana B. and Aguiar, Lucas A. and Fonseca, Felipe S. and Caminhas, Walmir M.},
 journal = {Applied Soft Computing}
}
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