文献类型：期刊文献

英文题名：Rolling Bearing Incipient Fault Detection via Optimized VMD Using Mode Mutual Information

作者：Tan, Shuai[1];Wang, Aimin[1];Shi, Hongbo[1];Guo, Lei[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2022

卷号：20

期号：4

起止页码：1305

外文期刊名：INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS

收录：;EI(收录号：20221411906755);WOS:【SCI-EXPANDED(收录号:WOS:000777404900024)】；

基金：This research is supported by the National Natural Science Foundation of China (No.62073141), the National Key Research and Development Program of China (No.2020YFC1522505, No.2020YFC1522502), and Shanghai Natural Science Foundation (No.22ZR1417000).

语种：英文

外文关键词：Cuckoo search; fault detection; mutual information; rolling bearing; variational mode decomposition

摘要：The complete failure of the rolling bearing is a deterioration process from the incipient weak fault to the severe fault, thus it is important to alarm when the incipient fault appear. This work presents a novel incipient bearing fault diagnosis framework using mode mutual information (MMI) based fitness function, variational mode decomposition (VMD), and cuckoo search (CS) algorithm. MMI based fitness function is proposed in order to obtain the optimal combinations of the VMD parameters. Therefore, the optimal parameters of VMD can be obtained by CS algorithm using proposed fitness function. Afterwards, a vibration signal is decomposed into a set of modes using the optimal VMD, and the kurtosis value of all modes are calculated. The envelop of the mode with maximum kurtosis value between modes and raw signal is computed as the input vector of the stacked denoised autoencoder (SDAE). Comparisons have been conducted via SDAE to evaluate the performance by using EMD and the fixed-parameter VMD. The experimental results demonstrate that the proposed method is more effective in extracting the incipient bearing fault characteristics.