Bearing fault detection using artificial neural networks and genetic algorithm

Authors:
B. Samanta;Khamis R. Al-Balushi;Saeed A. Al-Araimi
Affiliations:
Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, Muscat, Sultanate of Oman;Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, Muscat, Sultanate of Oman;Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, Muscat, Sultanate of Oman
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2004

Citing 9
Cited 4

Multilayer feedforward networks are universal approximators

Neural Networks
Probabilistic neural networks

Neural Networks
Approximation and radial-basis-function networks

Neural Computation
Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Advanced Methods in Neural Computing

Advanced Methods in Neural Computing
Neural Networks: A Comprehensive Foundation (3rd Edition)

Neural Networks: A Comprehensive Foundation (3rd Edition)
Guest Editorial Special Issue on Artificial Neural Networks and Statistical Pattern Recognition

IEEE Transactions on Neural Networks

MLP, PNN and fuzzy logic improved by genetic algorithms in fault detection and isolation

ISC '07 Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control
Multiclass classification based on extended support vector data description

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Layered Fault Management Scheme for End-to-end Transmission in Internet of Things

Mobile Networks and Applications
A classifier fusion system for bearing fault diagnosis

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

A study is presented to compare the performance of besaring 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.