Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm
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Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm B. Samanta Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, Muscat 123, Sultanate of Oman Email: [email protected]
Khamis R. Al-Balushi Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, Muscat 123, Sultanate of Oman Email: [email protected]
Saeed A. Al-Araimi Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, Muscat 123, Sultanate of Oman Email: [email protected] Received 26 August 2002; Revised 22 July 2003; Recommended for Publication by Shigeru Katagiri A study is presented to compare the performance of bearing fault detection using three types of artificial neural networks (ANNs), namely, multilayer perceptron (MLP), radial basis function (RBF) network, and probabilistic neural network (PNN). The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to all three ANN classifiers: MLP, RBF, and PNN for two-class (normal or fault) recognition. The characteristic parameters like number of nodes in the hidden layer of MLP and the width of RBF, in case of RBF and PNN along with the selection of input features, are optimized using genetic algorithms (GA). For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine with and without bearing faults. The results show the relative effectiveness of three classifiers in detection of the bearing condition. Keywords and phrases: condition monitoring, genetic algorithm, probabilistic neural network, radial basis function, rotating machines, signal processing.
1. INTRODUCTION Machine condition monitoring is gaining importance in industry because of the need to increase reliability and to decrease the possibility of production loss due to machine breakdown. The use of vibration and acoustic emission (AE) signals is quite common in the field of condition monitoring of rotating machinery. By comparing the signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, rotor rub, shaft misalignment, gear failures, and bearing defects is possible. These signals can also be used to detect the incipient failures of the machine components, through the online monitoring system, reducing the possibility of catastrophic damage and the downtime. Some of the recent works in the area are listed in [1, 2, 3, 4, 5, 6, 7, 8]. Although often the visual inspection of the frequency domain features of the measured signals is ad-
equate to identify the faults, there is a need for a reliable, fast, and automated procedure of diagnostics. Artificial ne
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