Diagnosis of the industrial systems by fuzzy classification.
The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parametrization method which is not necessarily based on an iterative training. This approach can be seen as a pre-parametrization which allows the determination of the rules base and the parameters of the membership functions. We also present a continuous and derivable version of the previous classifier and suggest an iterative learning algorithm based on a gradient method. An example using the learning basis IRIS, which is a benchmark for classification problems, is presented showing the performances of this classifier. Finally this classifier is applied to the diagnosis of a dc motor showing the effectiveness of this method.
Toscano
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R
E.N.I.SE., Laboratoire de Tribologie et de Dynamique des Systèmes UMR 5513, 58 rue Jean Parot 42023 Saint-Etienne cedex 2, France. toscano@enise.fr
Lyonnet
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P
D000465:Algorithms; D001709:Biotechnology / Q000379:methods*; D003198:Computer Simulation; D003658:Decision Making, Computer-Assisted; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D019544:Equipment Failure Analysis / Q000379:methods; D017143:Fuzzy Logic; D010363:Pattern Recognition, Automated; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes