基于RCMRFDE和ELM的滚动轴承故障检测方法  

Fault diagnosis method of rolling bearing based on RCMRFDE and ELM

作　　者：周经龙 乔惠萍 ZHOU Jing-long;QIAO Hui-ping(Smart Academy,Peking University,Beijing 100091,China;School of Artificial Intelligence,Chongqing Creation Vocational College,Chongqing 402160,China;School of Computer Engineering,Shanxi Vocational University of Engineering Science and Technology,Jinzhong 030031,China)

机构地区：[1]北京大学智能学院,北京100091 [2]重庆科创职业学院人工智能学院,重庆402160 [3]山西工程科技职业大学计算机工程学院,山西晋中030031

出　　处：《机电工程》2023年第1期1-10,共10页Journal of Mechanical & Electrical Engineering

基　　金：重庆市教育委员会科学技术研究重点项目(KJZD-K201805401)。

摘　　要：为了有效地提升滚动轴承故障诊断的可靠性和准确性,鉴于精细复合多尺度反向波动散布熵(RCMRFDE)方法在描述非线性序列复杂度和特征提取方面的优势,提出了一种结合RCMRFDE和极限学习机(ELM)的滚动轴承综合故障检测方法(该方法包括健康检测和故障分类)。首先,根据滚动轴承健康和故障振动信号复杂性具有显著性差异的特点,利用RCMRFDE预先检测了滚动轴承的健康状态,筛选出了健康轴承;然后,采用RCMRFDE提取出了剩余故障轴承的故障特征,并采用极限学习机(ELM)对故障类型进行了智能化的识别;最后,基于两种公开的滚动轴承故障实验数据,分别采用RCMRFDE+ELM方法与其他5种故障诊断方法进行了检测,并将所得结果进行了比较分析,以验证新方法的检测精度、分类准确率、效率和可靠性。研究结果表明:采用RCMRFDE+ELM方法能够准确地检测出滚动轴承是否存在故障,并且在二次实验中,对于故障轴承的平均识别准确率分别达到了99.96%和99.67%,均高于其他方法。该方法可以为建立滚动轴承的健康监测模型提供阈值设置方法和诊断思路。In order to effectively improve the reliability and accuracy of rolling bearing fault diagnosis,in view of the advantages of refined composite multi-scale reverse fluctuation dispersion entropy(RCMRFDE)method in describing the complexity of nonlinear sequences and feature extraction,a comprehensive rolling bearing fault detection method combining RCMRFDE and extreme learning machine(ELM)was proposed(this method includes health detection and fault classification).Firstly,according to advantage of the significant difference between the health and fault vibration signal complexity of rolling bearing,RCMRFDE was used to detect the health state of rolling bearing in advance and screen out the healthy bearing.Then,RCMRFDE was used to extract the fault features of the remaining fault bearings,and the fault type was intelligently recognized based on the extreme learning machine(ELM).Finally,based on two kinds of published rolling bearing fault data,the RCMRFDE+ELM method was tested with other five fault diagnosis methods,and the obtained results were compared,to verify the detection accuracy,classification accuracy,efficiency and reliability of the new method.The research results show that the proposed method can accurately detect whether there is a fault in the rolling bearing,and the average recognition accuracy of the fault rolling bearing was 99.96%and 99.67%respectively,which were higher than other methods.It also provides a detection threshold setting scheme and a fault diagnosis idea for establishing the health monitoring model of rolling bearings.

关 键 词：轴承故障诊断 故障特征提取 轴承健康检测 故障分类 精细复合多尺度反向波动散布熵 极限学习机 综合故障检测 

分 类 号：TH133.33[机械工程—机械制造及自动化]