摘要
针对滚动轴承故障诊断中特征提取困难和模式识别准确率低等问题,提出了一种基于多尺度均值排列熵(MMPE)和灰狼优化支持向量机(GWO-SVM)结合的故障诊断方法。利用MMPE全面表征滚动轴承故障特征信息,选取适当维数特征构成样本数据集,采用GWO-SVM分类器进行故障模式识别。对所提基于MMPE和GWO-SVM故障诊断方法进行理论分析和研究,并利用滚动轴承试验数据进行相应对比试验分析,结果表明:MMPE能够有效提取滚动轴承故障特征信息;GWO-SVM识别准确率和识别速度优于滚动轴承故障诊断其它常用参数优化支持向量机;所提方法能够有效识别滚动轴承故障位置和故障程度,在滚动轴承数据集上取得了98.0%的故障识别准确率,高于基于MPE和GWO-SVM方法的97.0%准确率,并且在噪声背景下取得了93.5%的识别准确率,优于后者83.0%准确率,证明了所提MMPE具有更好的噪声鲁棒性。
Here,aiming at problems of difficult feature extraction and low accuracy of pattern recognition in rolling bearing fault diagnosis,a fault diagnosis method based on multi-scale mean permutation entropy(MMPE)and grey wolf optimized support vector machine(GWO-SVM)was proposed.Firstly,MMPE was used to comprehensively characterize rolling bearing fault feature information.Then,features with appropriate dimensions were selected to form a sample data set.Finally,GWO-SVM classifier was employed to do fault pattern recognition.The proposed fault diagnosis method based on MMPE and GWO-SVM was theoretically analyzed and studied,the corresponding test analyses were contrastively performed by using test data of rolling bearing.The results showed that MMPE can effectively extract the fault feature information of rolling bearing;the recognition accuracy and recognition speed of GWO-SVM are better than those of other commonly used parametric optimization SVMs of rolling bearing fault diagnosis;the proposed method can effectively identify fault position and fault degree of rolling bearing,the fault recognition accuracy based on MMPE and GWO-SVM is 98.0%using rolling bearing data set,it is higher than that based on MPE and GWO-SVM of 97.0%;the recognition accuracy based on MMPE and GWO-SVM is 93.5%under noise background,while that based on MPE and GWO-SVM is only 83.0%,so the proposed MMPE has better noise robustness.
作者
王贡献
张淼
胡志辉
向磊
赵博琨
WANG Gongxian;ZHANG Miao;HU Zhihui;XIANG Lei;ZHAO Bokun(School of Logistics Engineering,Wuhan University of Technology,Wuhan 430063,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2022年第1期221-228,共8页
Journal of Vibration and Shock
基金
上海交通大学舰船设备噪声与振动控制技术国防重点学科实验室开放课题基金(VSN201901)。
关键词
滚动轴承
故障诊断
多尺度均值排列熵
灰狼优化
支持向量机
rolling bearing
fault diagnosis
multi-scale mean permutation entropy(MMPE)
grey wolf optimization(GWO)
support vector machine(SVM)
