人工神经网络
支持向量机
滚动轴承
人工智能
方位(导航)
断层(地质)
模式识别(心理学)
要素(刑法)
计算机科学
工程类
地质学
地震学
声学
物理
政治学
法学
振动
作者
Sunil Tyagi,S. K. Panigrahi
出处
期刊:DOAJ: Directory of Open Access Journals - DOAJ
日期:2017-04-01
被引量:6
标识
DOI:10.22055/jacm.2017.21576.1108
摘要
A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here. The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditions. The time-domain vibration signals were divided into 40 segments and simple features such as peaks in time domain and spectrum along with statistical features such as standard deviation, skewness, kurtosis etc. were extracted. Effectiveness of SVM classifier was compared with the performance of Artificial Neural Network (ANN) classifier and it was found that the performance of SVM classifier is superior to that of ANN. The effect of pre-processing of the vibration signal by Discreet Wavelet Transform (DWT) prior to feature extraction is also studied and it is shown that pre-processing of vibration signal with DWT enhances the effectiveness of both ANN and SVM classifiers. It has been demonstrated from experiment results that performance of SVM classifier is better than ANN in detection of bearing condition and pre-processing the vibration signal with DWT improves the performance of SVM classifier.
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