基于IEWT和MCKD的滚动轴承故障诊断方法

针对经验小波变换（Empirical wavelet transform，EWT）对强噪声环境中滚动轴承微弱故障诊断的不足，主要是傅里叶频谱分段不当的问题。提出一种基于最大相关峭度解卷积（Maximum correlated kurtosis deconvolution，MCKD）降噪与改进EWT相结合的滚动轴承早期故障识别方法。首先采用最大相关峭度解卷积算法以包络谱的相关峭度最大化为目标对原信号进行降噪处理、检测信号中的周期性冲击成分，然后根据信号Fourier频谱的包络极大值进行分段，通过分析各频段平方包络谱中明显的频率成分来诊断故障。新方法能有效降噪、增强信号中周期性冲击特征、降低单次偶然冲击的影响、抑制非冲击成分。通过对含外圈、内圈故障的滚动轴承进行试验分析，结果表明，相比于快速谱峭度图和小波包络分析方法，该方法提取出的特征更加明显，能有效实现滚动轴承早期微弱故障的识别。

A Novel Fault Diagnosis Method Based on Improved Empirical Wavelet Transform and Maximum Correlated Kurtosis Deconvolution for Rolling Element Bearing

In order to solve the problem of Empirical wavelet transform method for the rolling element bearing fault diagnosis in strong noise condition, that is mainly the inappropriate segmentation of the signal spectrum, the combination of the Improved Empirical wavelet transform and Maximum correlated kurtosis deconvolution method is proposed. Firstly, an original signal is de-noised with MCKD algorithm, and the max kurtosis of its envelope spectrum is taken as an objective to detect its periodic impact components. Then, the envelope of the signal Fourier spectrum is segmented based on the peaks, and the most meaningful component can be found from the signal components. At last, fault features can be diagnosed by analyzing obvious frequency components in squared envelope spectrum. The new method can de-noise the signal and enhance the periodic impact components feature. It is shown that the lesser powerful fault induced by Single accidental impact and nonimpact components is restrained in de-noised signal. The effectiveness of the proposed method has been validated by both simulated and experimental bearing vibration signals. It's shown that fault character extracted by the proposed method is more clearly and believable than the fast kurtogram algorithm and wavelet envelope analysis.