高技术通讯2025，Vol.35Issue(1)：73-84,12.DOI:10.3772/j.issn.1002-0470.2025.01.008
基于参数优化变分模态分解和马田系统的工业缝纫机故障诊断方法
Fault diagnosis method of industrial sewing machines based on parameter optimization VMD and MTS
摘要
Abstract
Aiming at the problems that low accuracy and time-consuming in the traditional way of human ear listening in the factory quality inspection of industrial sewing machine,a fault diagnosis method of industrial sewing machine based on parameter optimization variational mode decomposition(VMD)and Mahalanobis-Taguchi system(MTS)is proposed.First,the relevant parameters of the variational mode decomposition are iteratively optimized by the salp swarm algorithm(SSA),and the VMD with the optimal parameters is used to decompose the sound signal of industrial sewing machines,so as to obtain the intrinsic mode function(IMF)with different central frequency.Then,the multi-domain feature fusion of IMF components is performed separately,and a reference space of MTS is constructed with normal samples,and a small number of fault samples are used to verify and optimize the reference space.Finally,combined with the threshold of Mahalanobis distance,the accurate fault identification and classifi-cation is achieved.Through the comparative analysis of simulation signals,it is proved that the SSA-VMD algorithm is feasible and superior in decomposing signals.The research results of experimental data and measured data show that the proposed fault diagnosis method has certain practical application value.关键词
工业缝纫机/故障诊断/变分模态分解/马田系统/多域特征融合Key words
industrial sewing machine/fault diagnosis/variational mode decomposition/Mahalanobis Tagu-chi system/multi-domain feature fusion引用本文复制引用
周中华,刘祖斌..基于参数优化变分模态分解和马田系统的工业缝纫机故障诊断方法[J].高技术通讯,2025,35(1):73-84,12.基金项目
浙江省自然科学基金探索项目(LY20A040007)资助. （LY20A040007）
