中国机械工程Issue(20)：2778-2783,6.DOI:10.3969/j.issn.1004-132X.2015.20.014
基于主成分分析和支持向量机的滚动轴承故障特征融合分析
Rolling Bearing Fault Feature Fusion Based on PCA and SVM
摘要
Abstract
To effectively reduce the dimension of rolling bearing fault features and improve the ac-curacy of diagnosis,the PCA and SVM were applied in the fusion of bearing fault features,and the cor-responding decision-making process was presented.By using the fault feature extraction algorithm and eigenvector constructing methods which were proposed based on wavelet packet decomposition,the bearing vibration signals in different states were decomposed to get the 8-dimensional feature sets which could be used to characterize the running conditions of the bearing.The cumulative contribution rate of 95% principal components were extracted by using PCA method and were input into SVM clas-sifier for identification.Results show that the fault feature dimensions of rolling bearing can be re-duced from 8-dimensions to 5-dimensions,which can still characterize the bearing status effectively, and the computational complexity can be reduced.The fault diagnosis accuracy is higher than 97%,and the diagnosis time is short relatively.The identification accuracy of four bearing status from high to low in turn is normal,outer ring peel,roller peel and inner ring peel.It can ensure the safe operation of the equipment and provide theoretical basis for fast fault diagnosis.关键词
主成分分析/支持向量机/特征融合/故障诊断/滚动轴承Key words
principal component analysis (PCA)/support vector machine(SVM)/feature fusion/fault diagnosis/rolling bearing分类
资源环境引用本文复制引用
古莹奎,承姿辛,朱繁泷..基于主成分分析和支持向量机的滚动轴承故障特征融合分析[J].中国机械工程,2015,(20):2778-2783,6.基金项目
国家自然科学基金资助项目(61164009,61463021) （61164009,61463021）
江西省自然科学基金资助项目(20132BAB206026) （20132BAB206026）
江西省教育厅科学技术研究项目(GJJ14420) （GJJ14420）
江西省青年科学家培养对象计划资助项目(20144BCB23037) （20144BCB23037）
