基于小波分析的滚动轴承的故障特征提取技术  

Rolling Bearing's Breakdown Feature Extraction Technology Based on Wavelet Analysis

作　　者：刘春光[1] 谭继文[1] 张驰[1] 

机构地区：[1]青岛理工大学,山东青岛266033

出　　处：《机械工程与自动化》2010年第2期127-128,131,共3页Mechanical Engineering & Automation

摘　　要：提出了一种新的滚动轴承电流信号的故障特征提取方法,利用电流传感器把测得的电流信号转变成电压信号,并对该信号进行小波降噪处理,有效地剔除噪声的干扰,提高了信号的信噪比。用小波分析提取降噪后电流信号的能量特征参数,以表征滚动轴承故障特征,在频谱图中建立起故障频带能量与滚动轴承状态的映射关系,为进一步应用神经网络进行故障诊断奠定了基础。This article proposes a new method extracting the breakdown characteristic from rolling beating＇s electric current signal. The electric current signal is transformed into voltage signal by an electric current sensor, and then the signal is processed by wavelet denoising, to enhance the signal-to-noise ratio. The energy characteristic parameter of the denoised signal attributes the rolling hearing＇s breakdown characteristic. This paper establishes the relationship of the breakdown frequency band energy and the rolling bearing condition in the spectrograph, which provides the basis for failure dignosis based on neural network.

关 键 词：滚动轴承 小波分析 故障特征 

分 类 号：TH133.31[机械工程—机械制造及自动化]