基于无源振动传感器标签的轴承故障在线诊断技术  

On-line fault diagnosis technology for bearings based on passive vibration sensor tag

作　　者：肖艳霞 田杰[1] 何怡刚[2] 汪涛[2] XIAO Yan-xia;TIAN Jie;HE Yi-gang;WANG Tao(School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China;School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

机构地区：[1]合肥工业大学机械工程学院,安徽合肥230009 [2]合肥工业大学电气与自动化工程学院,安徽合肥230009

出　　处：《传感器与微系统》2018年第5期55-57,60,共4页Transducer and Microsystem Technologies

基　　金：国家自然科学基金资助项目(51577046);国家自然科学基金重点资助项目(51637004);国家重点研发计划"重大科学仪器设备

摘　　要：提出了一种基于无源振动传感器标签的穿梭车轴承故障在线诊断技术。设计了一种无源射频识别(RFID)振动传感器标签结构,因其工作在无源模式下,减少了在线故障诊断的成本,同时可以实现对轴承故障的长期在线诊断。介绍了振动信号的处理方式,提出了基于奇异熵的奇异值分解信号降噪算法,依据信号的奇异熵自行定阶降噪,避免了人为预设参数所导致的误差,并提出了基于最小二乘支持向量回归(LS-SVR)的故障诊断算法。测试结果表明:设计的标签能够可靠地完成信号采集和传输,采用的算法能够快速而准确地定位故障,较传统故障诊断方法提高了实时性并降低了成本。An on-line bearing fault diagnosis technique based on passive vibration sensor tag is proposed. Structure of a novel passive radio frequency identification （RFID） vibration sensor tag is designed, which can realize longt time and on-line fault diagnosis and decrease cost due to its passive operation mode. Processing mode of vibration signal is introduced. Singular value decomposition（SVD） signal denoising algorithm based on singular entropy is proposed, according to singular entropy of signal to determine order by itself and denoise, avoid error caused by man-made preset parameters, and fault diagnosis algorithm based on least squares support vector regression （LS-SVR） is proposed. Test results show that the designed tag can achieve signal acquision and transmission reliably,the employed algorithm can locate fault fastly and accurately, which improves the real-time performance and decrease the cost compared with traditional fault diagnosis method.

关 键 词：轴承 射频识别 故障诊断 奇异值分解 奇异熵 最小二乘支持向量回归 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]