作者：谢晖, 傅攀, 陈侃

作者单位：西南交通大学机械工程学院, 四川成都 610031

关键词：离散小波变换; Hilbert变换; 故障特征; 故障诊断; 数据采集

摘要：

滚动轴承是机械设备中极易损坏的零部件。旋转机械的故障有百分之三十是由轴承故障引起的,因此引入轴承故障诊断对于机械化生产和作业来说是十分必要的。在对轴承故障的机理和特征全面了解的基础上,利用了离散小波变换对不同工矿下的原始信号进行分解,然后对小波分解出的近似和细节信号进行Hilbert变换,利用得出的信号可以较好地提取故障特征。

Fault diagnosis of rolling bearings based on wavelet

XIE Hui, FU Pan, CHEN Kan

School of Mechanical and Engineering, Southwest Jiaotong University, Chengdu 610031, China

Abstract: Rolling bearing is an easily damaged part of mechanical equipments. It was reported that 30% rotating machinery faults were caused by the faults of rolling bearings, therefore, it is very necessary to introduce fault diagnosis of rolling bearings into those fields of mechanized production and operations. According to comprehensive understanding of the mechanism and characteristics for faults of rolling bearings, those fault features were extracted well from those Hilbert transform signals transformed from the approximate signals and details which were the results of decomposing the initial signals in different work conditions through DWT(Discrete Wavelet Transform).

Keywords: DWT; Hilbert transform; Failure character; Fault diagnosis; Data acquisition

2008, 34(2): 124-126,144  收稿日期: 2007-8-15;收到修改稿日期: 2007-11-2

基金项目: 

作者简介: 谢晖(1981-),男,硕士研究生,专业方向为人工智能设备工况状态监测与故障诊断。

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