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Báo cáo hóa học: "Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm"
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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm | EURASIP Journal on Applied Signal Processing 2004 3 366-377 2004 Hindawi Publishing Corporation 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 samantab@squ.edu.om 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 kbalushi@squ.edu.om 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 alaraimi@squ.edu.om 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 .