Gear fault detection using artificial neural networks and support vector machines with genetic algorithms

A study is presented to compare the performance of gear fault detection using artificial neural networks (ANNs) and support vector machines (SMVs). The time-domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to both classifiers based on ANNs and SVMs for two-class (normal or fault) recognition. The number of nodes in the hidden layer, in case of ANNs, and the radial basis function kernel parameter, in case of SVMs, along with the selection of input features are optimised using genetic algorithms (GAs). For each trial, the ANNs and SVMs are trained with a subset of the experimental data for known machine conditions. The trained ANNs and SVMs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The roles of different vibration signals, obtained under both normal and light loads, and at low and high sampling rates, are investigated. The results compare the effectiveness of both types of classifiers without and with GA-based selection of features and the classifier parameters. For most of the cases considered, the classification accuracy of SVM is better than ANN, without GA. With GA-based selection, the performance of both classifiers are comparable, in most cases, with three selected features. However, for SVMs with six features, 100% classification success is achieved in all test cases. The training time of SVMs is substantially less compared to ANNs in all cases considered. The present classification accuracy compares well with the results reported in a recent work, (Mech. Systems Signal Process. 16 (2002) 373), though the data and the feature sets are different.