Fault detection in dynamic systems by a Fuzzy/Bayesian network formulation

• This new approach is based on a Fuzzy/Bayesian representation.
• In this type of strategy to combine the potential for aggregation of information/knowledge of the fuzzy sets theory for processing input uncertainties in the Bayesian network simplicity we will call this combination of fuzzy/Bayesian network.
• To illustrate the efficiency of the proposed methodology, the problem of fault detection in the stator winding of induction machine is presented.
• The new strategy is based on the potential for aggregation of information/knowledge the fuzzy sets theory for processing input uncertainties in the Bayesian network.

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.