Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine
Expert Systems with Applications: An International Journal
Application of an intelligent classification method to mechanical fault diagnosis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Optimal MLP neural network classifier for fault detection of three phase induction motor
Expert Systems with Applications: An International Journal
Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Unitary anomaly detection for ubiquitous safety in machine health monitoring
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Expert Systems with Applications: An International Journal
Computational Intelligence and Neuroscience
Expert Systems with Applications: An International Journal
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This paper proposes a systematic procedure based on a pattern recognition technique for fault diagnosis of induction motors bearings through the artificial neural networks (ANNs). In this method, the use of time domain features as a proper alternative to frequency features is proposed to improve diagnosis ability. The features are obtained from direct processing of the signal segments using very simple calculation. Three different cases including, healthy, inner race defect and outer race defect are investigated using the proposed algorithm. The ANNs are trained with a subset of the experimental data for known machine conditions. Once the network is trained, efficiency of the proposed method is evaluated using the remaining set of data. The obtained results indicate that using time domain features can be effective in accurate diagnosis of various motor bearing faults with high precision and low computational burden.