中南大学学报（自然科学版）2011，Vol.42Issue(11)：3384-3391,8.
基于核主元分析的滚动轴承故障混合域特征提取方法
Mixed-domain feature extraction approach to rolling bearings faults based on kernel principle component analysis
摘要
Abstract
In order to effectively use the various nonstationary statistical features with significant differences from time domain, frequency domain and time-frequency domain, a novel mixed-domain feature extraction approach was proposed, which was based on kernel principle component analysis to improve the performance and efficiency for condition monitoring and fault diagnosis of rolling bearings. At first, the time-domain and frequency-domain features which were generated by the original signal, and time-frequency-domain features which were generated by the multi-resolution wavelet decomposition were extracted. The mixed-domain features set including 144 features were composed to characterize the original vibration signals. Then the kernel principle component analysis method was used to secondary extract the features which reflected sensitively the failure characteristics in the mixed-domain features set. According to the accumulated contribution rate of more than 90%, the first 11 nonlinear principal components were extracted as primary feature vector for support vector machine classifier to recognize. The results show that the mixed-domain features set can reflect the failure characteristics more comprehensively and accurately than a single feature or single-domain features. Kernel principle component analysis method can effectively reduce the input feature dimensions, and ensure the output features to be of high sensitivity to reflect the operational status of bearings and high separabilityfor pattern recognition. Compared to the common feature extraction method based on wavelet decomposition, this proposed method becomes more apparent to extract fault feature of rolling bearing in different types and degrees under different operating conditions.关键词
混合域/特征提取/核主元分析/故障检测/轴承Key words
mixed-domain/ feature extraction/ kernel principal component analysis/ fault detection/ rolling bearings分类
信息技术与安全科学引用本文复制引用
彭涛,杨慧斌,李健宝,姜海燕,魏巍..基于核主元分析的滚动轴承故障混合域特征提取方法[J].中南大学学报（自然科学版）,2011,42(11):3384-3391,8.基金项目
国家自然科学基金资助项目(60774069) （60774069）
省部级重点基金资助项目(9140A17051010BQ0104) （9140A17051010BQ0104）
中国博士后科学基金资助项目(20070410462) （20070410462）
湖南省教育厅科技计划项目(07C005) （07C005）
