基于TESPAR和LS-SVM算法的滚动轴承退化趋势预测被引量：9

Degradation Trend Prediction of Rolling Bearings Based on TESPAR and LS-SVM Algorithm

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摘要在分析了滚动轴承退化趋势预测是实现滚动轴承预防维护关键的基础上,提出了基于时间编码信号处理与识别(TESPAR)和最小二乘支持向量机(LS-SVM)相结合的滚动轴承性能退化趋势预测新方法。将通过TESPAR提取的性能退化特征集输入到LS-SVM中完成了滚动轴承的性能退化趋势预测,最后通过实例验证了所提出方法的有效性。 The prediction of the degradation trend of rolling bearing maintenance of rolling bearing, a new method of rolling bearing degradatio time-coded signal processing and recognition （TESPAR） is the key to realize the n trend prediction based on and least squares support vector machine（LS- are predicted by inputting the performance degradation feature set extracted by TESPAR into LS-SVM. Finally, the effectiveness of the proposed method is verified by an example.

作者陈龙谭继文姜晓瑜 CI-IEN Long TAN Ji-wen JIANG Xiao-yu(Qingdao University of Technology, Qingdao 266520, Chin)

机构地区青岛理工大学

出处《煤矿机械》 2017年第8期18-20,共3页 Coal Mine Machinery

基金国家自然科学基金项目(51075220) 高等学校博士学科点专项科研基金(20123721110001)

关键词滚动轴承 TESPAR LS-SVM 退化趋势预测 rolling bearing TESPAR LS-SVM degradation trend prediction

分类号 TH132.2 [机械工程—机械制造及自动化] TH17 [机械工程—机械制造及自动化]

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