Transient impulses caused by local faults are critical informative indicators for rolling element bearing fault diagnosis. The methods for accurately extracting transient impulses while suppressing strong background noise and interference components have received extensive studies. In this article, a novel fault diagnosis scheme based on optimized wavelet packet denoising and modulation signal bispectrum is proposed, which takes advantage of the transient impulse enhancement of wavelet packet denoising and the demodulation ability of modulation signal bispectrum to diagnose bearing faults more accurately. First, the measured signals are decomposed into a series of time–frequency subspaces using wavelet packet transform. An optimal threshold value is selected based on the proposed threshold criterion by considering unbiased autocorrelation of envelope and Gini index of the transient impulses. Subsequently, the subspaces are denoised by the wavelet packet denoising with the optimized threshold value, and the master subspaces that containing the fault-related transient impulses are selected based on the Gini index indicator. Finally, the modulation signal bispectrum is utilized to further purify the signal and extract the modulation components contained in the transient impulses, and the suboptimal modulation signal bispectrum slices are selected based on the characteristic frequency intensity coefficient. The modulation signal bispectrum detector is then obtained by averaging the suboptimal modulation signal bispectrum slices to determine the type of the bearing faults. The proposed wavelet packet denoising-modulation signal bispectrum is validated based on the simulation and experimental studies. Compared with the variational mode decomposition and Teager energy operator, fast kurtogram as well as conventional modulation signal bispectrum, the proposed wavelet packet denoising-modulation signal bispectrum method has superior performance in extracting the fault feature of the incipient defects on different bearing components.

由局部故障引起的瞬态脉冲是滚动轴承故障诊断的关键信息指标。在抑制强背景噪声和干扰成分的同时准确提取瞬态脉冲的方法得到了广泛的研究。本文提出了一种基于优化小波包去噪和调制信号双谱的故障诊断新方案，利用小波包去噪的瞬态脉冲增强和调制信号双谱的解调能力更准确地诊断轴承故障。首先，使用小波包变换将测量信号分解为一系列时频子空间。通过考虑瞬态脉冲的包络和基尼指数的无偏自相关，基于所提出的阈值标准选择最佳阈值。随后，利用优化阈值的小波包去噪对子空间进行去噪，并根据基尼指数指标选择包含故障相关瞬态脉冲的主子空间。最后，利用调制信号双谱进一步净化信号，提取瞬态脉冲中包含的调制分量，根据特征频率强度系数选择次优调制信号双谱切片。然后通过对次优调制信号双谱切片求平均以确定轴承故障的类型来获得调制信号双谱检测器。所提出的小波包去噪调制信号双谱在仿真和实验研究的基础上得到验证。与变分模态分解和Teager能量算子、快速峰图以及常规调制信号双谱相比，所提出的小波包去噪-调制信号双谱方法在提取不同轴承部件早期缺陷的故障特征方面具有优越的性能。