中国舰船研究2025，Vol.20Issue(2)：68-76,9.DOI:10.19693/j.issn.1673-3185.04057
基于多域信息融合与改进ELM的船舶电机轴承故障诊断
Fault diagnosis of ship motor bearings based on multi-domain information fusion and improved ELM
摘要
Abstract
[Objectives]Aiming at the problems that the symptom parameters from monitoring signals in a single analysis domain fail to fully characterize the operating state of the monitored object,and the model parameters of the Extreme Learning Machine(ELM)network are difficult to achieve the optimization,a fault diagnosis method for ship motor bearings is proposed,based on multi-domain information fusion and an im-proved ELM.[Methods]First,a multi-domain feature parameter set was constructed from the vibration signals of ship motor bearings in the time domain,frequency domain and time-frequency domain.This set served as the input to the fault diagnosis model.The sparrow search algorithm was then used to optimize the model parameters of the ELM network by determining the optimal weights and thresholds,thus enhancing the fault diagnosis accuracy of ELM model.Finally,the fault states of motor bearings were identified using experi-mental data from a self-built test bench and open-source experimental datasets.[Results]Experimental data verification based on the marine motor test bench demonstrated that the fault diagnosis model using multi-domain feature parameter sets,achieved a recognition accuracy of 100%on both the training and test sets.Verification with open-source experimental data showed that the recognition accuracy on the test set for the improved ELM model was 90.5%,which is 12.7%higher than that of the original ELM model.Additionally,the recognition accuracies on both training and test sets were higher than those of other diagnostic models.[Conclusions]This study has improved the input symptom parameter set and the diagnosis model.The pro-posed method can effectively identify the fault states of motor bearings and demonstrates good model stability,providing a valuable reference for fault diagnosis of ship motor bearings.关键词
电动机/轴承/故障分析/故障诊断/多域信息融合/麻雀搜索算法/极限学习机Key words
electric motors/bearing(machine parts)/failure analysis/fault diagnosis/multi-domain in-formation fusion/sparrow search algorithm/extreme learning machine分类
交通工程引用本文复制引用
戈淳,闫灶宇,商嘉桐,薛红涛..基于多域信息融合与改进ELM的船舶电机轴承故障诊断[J].中国舰船研究,2025,20(2):68-76,9.基金项目
国家自然科学基金资助项目(52272367) （52272367）
