基于EMD间隔阈值消噪与极大似然估计的滚动轴承故障诊断方法

Fault diagnosis of rolling bearings based on EMD Interval-Threshold denoising and maximum likelihood estimation

针对小波阈值消噪用作滚动轴承故障信号处理存在小波基函数较难选择及传统硬阈值、软阈值消噪效果差等缺点,将EMD间隔阈值消噪与极大似然估计相结合,应用于滚动轴承早期微弱故障诊断。对原始信号进行EMD分解;对各IMF进行基于极大似然估计的间隔阈值消噪,并重构出故障信号;进行包络谱分析得出诊断结果。数字仿真信号与实验信号验证了该方法的有效性。

Wavelet thresholding as a common signal processing method for fault diagnosis of rolling bearing has the deficiency of difficulty to choose basic function and the weakness of poor denoising performance by using conventional soft or hard threshold. A method combining EMD interval-thresholding with maximum likelihood estimation to diagnose the incipient weak fault of rolling bearing was presented. The original signal was analyzed by empirical mode decomposition (EMD), then each intrinsic modal function (IMF) was denoised by interval-thresholding based on maximum likelihood estimation and the fault signals was acquired by reconstructing the thresholded IMFs. Finally, the results were achieved by envelope spectrum analysis of denoised signal. The results of numerical simulation and an industrial case show that the proposed method is effective to diagnose the fault of rolling bearing significantly.