小波降噪及Hilbert变换在电机轴承故障诊断中的应用  

Application of wavelet denoising and Hilbert transform in fault diagnosis of motor bearing

作　　者：丁锋[1] 秦峰伟 DING Feng QIN Feng-wei(Department of Mechanical and Electronic Engineering, Xi＇an Technological University, Xi＇an 710021, China)

机构地区：[1]西安工业大学机电工程学院,陕西西安710021

出　　处：《电机与控制学报》2017年第6期89-95,共7页Electric Machines and Control

基　　金：国家自然科学基金(51275374);国防科技重点实验室开放基金

摘　　要：针对振动信号降噪处理及故障特征提取是机械故障诊断的重点问题,为了有效消除高频信号的影响,并充分提取出电机轴承的低频故障特征。提出利用小波降噪及Hilbert变换的方法对采集的电机轴承振动数据进行处理并提取其故障特征信息。首先,运用小波降噪对采集到的振动数据进行降噪处理,抑制噪声干扰,然后对其进行Hilbert变换解调出故障特征频率。通过对现场测取的轴承振动数据进行信号处理可以达到理想的诊断效果,由此得知,该方法能通过电机轴承振动信号进行故障特征信息处理,有效地进行轴承故障分析及诊断。Vibration signal denoising and fault feature extraction is the key focus of mechanical fault diagnosis, in order to effectively eliminate the impact of high frequency vibration signals, and fully extract the low frequency fault characteristics of motor bearings. A method that combined Wavelet denoising with Hil- bert transform was put forward to deal with and analyze the vibration signals measured from motor bearing to extract fault feature. Firstly, the wavelet denoising was applied to rotary mechanical bearing data to suppress the noise interference. Then, Hilbert transform was used to deal with the denoised signal to extract the fault feature. On the basis of bearing vibration data acquisition on site and by the signal processing, the ideal effect of diagnosis can be achieved. Thus it is known that the method can process fault characteristic information by the bearing vibration signal, and bearing fault analysis and diagnosis is implemented effectively.

关 键 词：轴承 振动信号 小波消噪 HILBERT变换 

分 类 号：TH111.3[机械工程—机械设计及理论]