[其它]Gear fault detection using artificial neural networks and support vector machines

Abstract T{1Z(M+  
A study is presented to compare the performance of gear fault detection using artificial neural networks y{~l&zrl  
(ANNs) and support vector machines (SMVs). The time-domain vibration signals of a rotating machine c;w%R8z  
with normal and defective gears are processed for feature extraction. The extracted features from original o|\0IG(\  
and preprocessed signals are used as inputs to both classifiers based on ANNs and SVMs for two-class <<@vy{*Hg  
(normal or fault) recognition. The number of nodes in the hidden layer, in case of ANNs, and the radial 8~QEJW$  
basis function kernel parameter, in case of SVMs, along with the selection of input features are optimised sK~d{)+T  
using genetic algorithms (GAs). For each trial, the ANNs and SVMs are trained with a subset of the ZyBNo]  
experimental data for known machine conditions. The trained ANNs and SVMs are tested using the 4}:a"1P"  
remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The [KIK}:  
roles of different vibration signals, obtained under both normal and light loads, and at low and high >gAq/'.Q  
sampling rates, are investigated. The results compare the effectiveness of both types of classifiers without <Z^t^ O  
and with GA-based selection of features and the classifier parameters. For most of the cases considered, the @\,WJmW  
classification accuracy of SVM is better than ANN, without GA. With GA-based selection, the V j\1HQ  
performance of both classifiers are comparable, in most cases, with three selected features. However, for :eQ?gM!,  
SVMs with six features, 100% classification success is achieved in all test cases. The training time of SVMs y6|&bJ @  
is substantially less compared to ANNs in all cases considered. The present classification accuracy n>F1G MX  
compares well with .. gn&jNuGg  
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