Application of the fuzzy min–max neural network to fault detection and diagnosis of induction motors
作者： Manjeevan Seera , Chee Peng Lim , Dahaman Ishak , Harapajan Singh
DOI: 10.1007/S00521-012-1310-X
关键词: Artificial intelligence 、 Signature (logic) 、 Computer science 、 Artificial neural network 、 Induction motor 、 Fuzzy logic 、 Finite element method 、 Harmonics 、 Fault detection and isolation 、 Machine learning 、 Control theory 、 Stator
摘要: In this paper, an application of the motor current signature analysis (MCSA) method and fuzzy min–max (FMM) neural network to detection classification induction faults is described. The finite element employed generate simulated data pertaining changes in stator signatures under different conditions. MCSA then used process signatures. Specifically, power spectral density extract harmonics features for fault with FMM network. Various types faults, which include winding eccentricity problems, load conditions are experimented. results analyzed compared those from other methods. outcomes indicate that proposed technique effective diagnosis motors
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