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Guest editorial special section on motor fault detection and diagnosis
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This article is published in IEEE Transactions on Industrial Electronics.The article was published on 2000-10-01. It has received 50 citations till now. The article focuses on the topics: Fault detection and isolation & Preventive maintenance.read more
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Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis
TL;DR: Two neural fuzzy (NN/FZ) inference systems, namely, fuzzy adaptive learning control/decision network (FALCON) and adaptive network based fuzzy inference system (ANFIS), with applications to induction motor fault detection/diagnosis problems are presented.
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Airgap-Eccentricity Fault Diagnosis, in Three-Phase Induction Motors, by the Complex Apparent Power Signature Analysis
TL;DR: The use of the signature analysis of the complex apparent power modulus as a new technique for the diagnostics of mixed airgap-eccentricity condition in operating three-phase squirrel-cage induction motors is presented.
Electromagnetic flux monitoring for detecting faults in electrical machines
TL;DR: In this paper, the ability of the electromagnetic flux measured in various locations of a 35-kW cage induction motor to provide useful information about faults was investigated and the use of this monitoring parameter was assessed in comparison with some other electrical parameters used for fault detection, such as stator phase current and circulatin g currents between the parallel branches of the stator winding.
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Soft computing methods in motor fault diagnosis
Xiao-Zhi Gao,Seppo J. Ovaska +1 more
TL;DR: An overview on the recent developments in the emerging field of soft computing-based electric motor fault diagnosis using neural networks, fuzzy logic, neural-fuzzy, and genetic algorithms is given.
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Induction machine fault detection using stray flux EMF measurement and neural network-based decision
TL;DR: In this article, the performance of voltage unbalance and rotor fault detections using an external stray flux sensor in a working three-phase induction machine was evaluated using a neural network approach based on a multi-layer perceptron (MLP) structure.