重庆理工大学学报2024，Vol.38Issue(9)：286-294,9.DOI:10.3969/j.issn.1674-8425(z).2024.05.036
一种WTMSST结合自适应参数VMD的滚动轴承故障诊断
Fault diagnosis of rolling bearings based on WTMSST and adaptive parameter VMD
摘要
Abstract
To address the common problems in the conventional time-frequency analysis methods used in current bearing fault diagnosis, such as relatively discrete transform coefficient distribution on the time-frequency plane and blurry energy in the time-frequency spectrum, this paper proposes a rolling bearing fault diagnosis method based on wavelet transform modulated synchronous squeezing transform ( WTMSST) in conjunction with variational mode decomposition ( VMD) optimized by dung beetle optimizer ( DBO) .First, the method adopts a WTMSST algorithm optimized by the weighted time-synchronous squeezing transform ( WTSST ) , reducing the group delay under strong frequency changes through fixed-point iteration.Then, by employing the smallest envelope entropy as the fitness function, the DBO algorithm optimizes the input parameters of VMD.Following the reconstruction of the signal based on kurtosis, the WTMSST time-frequency analysis method is employed for fault feature extraction.Experiments are conducted using the Case Western Reserve University data set.Tests are conducted using the data set of Case Western Reserve University.Our results show the method accurately describes the impact characteristics of the signal and performs better than the previous processing methods.关键词
故障诊断/时间重分配同步压缩变换/固定点迭代/变分模态分解/蜣螂算法Key words
fault diagnosis/time redistribution synchronous compression transformation/fixed point iteration/variable mode decomposition/dung beetle algorithm分类
机械工程引用本文复制引用
施天惠,黄民..一种WTMSST结合自适应参数VMD的滚动轴承故障诊断[J].重庆理工大学学报,2024,38(9):286-294,9.基金项目
工信部高质量发展项目(ZTZB-22-009-001) （ZTZB-22-009-001）
