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

Abstract /UeLf$%ZW  
A study is presented to compare the performance of gear fault detection using artificial neural networks Oq"(oNG@  
(ANNs) and support vector machines (SMVs). The time-domain vibration signals of a rotating machine A^7!:^%K  
with normal and defective gears are processed for feature extraction. The extracted features from original eeIh }t>[  
and preprocessed signals are used as inputs to both classifiers based on ANNs and SVMs for two-class z9:@~3k.  
(normal or fault) recognition. The number of nodes in the hidden layer, in case of ANNs, and the radial _3$@s{k-TI  
basis function kernel parameter, in case of SVMs, along with the selection of input features are optimised fr'M)ox1  
using genetic algorithms (GAs). For each trial, the ANNs and SVMs are trained with a subset of the t}
-[^|)7  
experimental data for known machine conditions. The trained ANNs and SVMs are tested using the ?]gZg[  
remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The 51yIW*  
roles of different vibration signals, obtained under both normal and light loads, and at low and high 0P`wh=")  
sampling rates, are investigated. The results compare the effectiveness of both types of classifiers without 7L)1mB.  
and with GA-based selection of features and the classifier parameters. For most of the cases considered, the g-'y_'%0G  
classification accuracy of SVM is better than ANN, without GA. With GA-based selection, the f])?Gw  
performance of both classifiers are comparable, in most cases, with three selected features. However, for '^'4C'J  
SVMs with six features, 100% classification success is achieved in all test cases. The training time of SVMs kTQ:k }%B  
is substantially less compared to ANNs in all cases considered. The present classification accuracy #1R_* Uh  
compares well with .. OJE<2:K  
{9TWPB/>  

未注册仅能浏览部分内容，查看全部内容及附件请先 登录 或 注册