压缩感知在装备故障预测与健康管理中的应用综述  

Application of Compressed Sensing in Equipment Fault Prediction and Health Management

作　　者：许波 马云飞 Xu Bo;Ma Yunfei(Department of Ordnance,Non Commissioned Officer Academy of PAP,Hangzhou 311403,China)

机构地区：[1]武警部队士官学校军械系,杭州311403

出　　处：《兵工自动化》2025年第1期15-22,共8页Ordnance Industry Automation

摘　　要：针对海量状态监测数据时存在采集频率要求高、采集系统负荷大、监测数据冗余等问题,通过对原始信号进行低维投影,突破Shannon-Nyquist采样定理的限制,极大缓解装备大数据造成的信息过载问题。围绕PHM 5层架构,分别从压缩感知(compressive sensing,CS)概念、以及在信号修复与降噪、故障诊断、退化状态识别中的应用几个角度,对已有成果进行总结,指明现有研究存在问题,提出相应的解决思路。该研究可为压缩感知的研究提供一定的参考。Aiming at the problems of high acquisition frequency,heavy load of acquisition system and redundancy of monitoring data in massive condition monitoring data,the limitation of Shannon-Nyquist sampling theorem is broken through by low-dimensional projection of original signals,which greatly alleviates the problem of information overload caused by large data of equipment.Based on the five-layer architecture of PHM,this paper summarizes the existing achievements from the aspects of the concept of compressive sensing(CS)and its application in signal restoration and noise reduction,fault diagnosis,and degradation state identification,points out the existing problems in the existing research,and puts forward the corresponding solutions.This study can provide some reference for the research of compressed sensing.

关 键 词：压缩感知 装备状态监测 数据采集 稀疏表示 故障诊断 

分 类 号：TH165.3[机械工程—机械制造及自动化]