基于压缩感知与改进的深度极限学习机的轴承故障诊断方法  

NEW METHOD FOR BEARING INTELLIGENT DIAGNOSIS BASED ON COMPRESSED SENSING AND MULTILAYER EXTREME LEARNING MACHINE

作　　者：陈万圣 王珍[1] 赵洪健[1] 王奉涛 CHEN WanSheng;WANG Zhen;ZHAO HongJian;WANG FengTao(School of Mechanical Engineering,University of Dalian,Dalian 116622,China;College of Engineering,University of Shantou,Shantou 515063,China)

机构地区：[1]大连大学机械工程学院,大连116622 [2]汕头大学工学院机电系,汕头515063

出　　处：《机械强度》2021年第4期779-785,共7页Journal of Mechanical Strength

基　　金：国家自然科学基金项目(51875075)资助。

摘　　要：大数据时代下的轴承故障监测存在海量数据处理实时性和故障特征选取的主观性问题,为了实时、智能的实现轴承故障诊断,提出了压缩感知(Compressed Sensing,CS)与改进的深度极限学习机(Multilayer Extreme Learning Machine,ML-ELM)相融合的轴承故障诊断新方法。该方法首先通过压缩感知理论从大量轴承监测数据中获取能够表达特征信息的少量数据,然后用轴承信号在压缩感知变换域中的测量值进行由PSO改进的深度极限学习机分类识别,实现故障智能诊断。此方法大幅减少了轴承诊断信号的数据量并省去了智能诊断时特征选取的步骤,充分利用了深度极限学习机从少量测量值中挖掘轴承信号的特征信息,实现了智能、准确的分类。实验分析表明:该方法对不同位置、不同损伤程度的故障能够准确的识别,为轴承状态监测和故障诊断提供了新方法。In the era of big data,bearing fault monitoring has the problem that it cannot realize the real-time processing of massive data processing and have the subjectivity about fault feature selection.In order to solve the above problems,a new bearing fault diagnosis method combining Compressed Sensing(CS)and Multilayer Extreme Learning Machine(ML-ELM)is proposed.This method first uses compressed sensing theory to obtain a small amount of data that can express characteristic information from a large amount of bearing monitoring data.Then multilayer extreme learning machine which improved by PSO use the measured value to obtain the classification information of bearing failure.This method greatly reduces the amount of data for bearing diagnostic signals and eliminate the step of feature selection in intelligent diagnosis.It makes full use of multilayer extreme learning machines to extract the characteristic information of bearing signals from a small number of observations.Experimental analysis show that this method can accurately identify faults at different positions and different damage levels,and provides a new method for bearing condition monitoring and fault diagnosis.

关 键 词：压缩感知 深度极限学习机 轴承 故障诊断 

分 类 号：TH133.33[机械工程—机械制造及自动化] TP277[自动化与计算机技术—检测技术与自动化装置]