改进小波算法在滚动轴承故障诊断的应用

在滚动轴承故障诊断中,为了提高诊断的准确率,需要对振动信号进行去噪预处理。小波变换具有良好的时频布局分析能力,在滚动轴承此类非平稳信号的去噪处理中得到了广泛应用。但是传统的阈值函数存在一定问题,影响去噪效果。提出了一种新的小波阈值函数,削弱了传统软硬阈值函数的缺陷,并且与已有的几种改进函数的去噪结果进行对比,证明了上述方法有很高的准确性和有效性,其去噪后的信噪比和均方根误差均优于软硬阈值和其它改进的阈值函数。

Application of Improved Wavelet Algorithm in Fault Diagnosis of Rolling Bearings

The complex vibration signal needs to be denoised in order to improve the accuracy in the fault diagno-sis of rolling bearings.Wavelet transform has good time-frequency layout analysis ability,and has been widely used in the denoising processing of non-stationary signals such as rolling bearings.However,the traditional threshold function has some drawbacks which affect the denoising effect.In this paper,a new wavelet is proposed.The thresh-old function can weaken the defects of the traditional soft and hard threshold functions,compared with other improved functions in the denoising results.It proves that the method has high accuracy and effectiveness.The signal-to-noise ratio and root mean square error after denoising are better than the soft and hard thresholds and other improved thresh-old functions.