In this work, genetic algorithm (GA), kernel based mutual information (KEMI) fitness function and variational mode decomposition (VMD) based strategy is proposed for the purpose of easy identification of single and multiple defects of bearing, both at fixed and varying speed. To make the multiple defects identification possible at varying speed, Fourier synchro squeezed transform (FSST) based processing is proposed to extract instantaneous frequency (IF) from the vibration signal itself. Extracted IF is used for converting time domain signal into angular domain. For finding optimizing parameters of VMD, KEMI based fitness function is developed. Thereafter, optimum parameters of VMD are found by GA using proposed fitness function. Then, optimized VMD is carried out. After, applying optimized VMD, KEMI of modes is calculated. Finally, envelope of mode having minimal KEMI is computed to find out the defect by comparing with defect order. It has been proved that selection of VMD parameters using kurtosis-based criteria can cause loss of defect features while decomposition, as a result defect order could not be identified in the envelope spectrum. The proposed method founds to outperform existing methods while extracting weak defect features.

在这项工作中，提出了遗传算法（GA），基于核的互信息（KEMI）适应度函数和基于变分模式分解（VMD）的策略，目的是易于识别固定和可变速度下的单个和多个轴承缺陷。 。为了使不同速度下的多个缺陷识别成为可能，提出了基于傅立叶同步压缩变换（FSST）的处理，以从振动信号本身中提取瞬时频率（IF）。提取的IF用于将时域信号转换为角域。为了找到VMD的优化参数，开发了基于KEMI的适应度函数。此后，使用拟定的适应度函数通过GA找到VMD的最佳参数。然后，执行优化的VMD。在应用优化的VMD之后，将计算模式的KEMI。最后，通过与缺陷顺序比较，计算出具有最小KEMI的模式的包络以找出缺陷。已经证明，使用基于峰度的标准来选择VMD参数会导致缺陷特征在分解时丢失，结果是无法在包络谱中识别缺陷顺序。所提出的方法在提取弱缺陷特征的同时发现其性能优于现有方法。