作　　者： ; ; ; ;

机构地区： 广州航海学院

出　　处： 《机床与液压》 2015年第3期185-188,共4页

摘　　要： 轴承早期故障的检测与诊断是实现安全生产、预防恶性事故的有效手段。用高精度加速度传感器采集轴承振动信号,采用小波软阈值降噪法剔除测试过程的噪声,提高采集信号的信噪比。基于小波变换奇异值检测技术,探讨了提取淹没在噪声背景中的早期故障特征的方法,同时指出了传统傅里叶变换的不足。研究表明,该方法是有效的,所提取的故障特征频率与理论计算的故障特征频率基本相同。研究结果为轴承早期故障检测与诊断提供了新途径。 The diagnosis of incipient fault of rolling bearing is the effective measure to realize safety production and to avoid major accident. By using high precision accelerometer to collect the vibration signals of bearing, the wavelet soft threshold noise reduction method was used to eliminate noise so as to enhance signal noise ration （SNR） of collected signal. Based on wavelet singularity detec- tion technology, the extraction of initial fault characteristics submerged in noise background method was discussed, at the same time the shortcomings of traditional Fourier transform were pointed out. Research shows that the method is effective, the extraction of fauh fea- ture frequency and the fault characteristic frequency from theoretical calculation are basically the same. Research results provide a new way for incipient fault detection and diagnosis of rolling bearing.

关 键 词： 轴承 早期故障诊断 小波变换 奇异性检测

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