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

作　　者：戈淳 闫灶宇 商嘉桐 薛红涛[1] GE Chun;YAN Zaoyu;SHANG Jiatong;XUE Hongtao(School of Automotive and Traffic Engineering,Jiangsu University,Zhengjiang 212013,China)

机构地区：[1]江苏大学汽车与交通工程学院,江苏镇江212013

出　　处：《中国舰船研究》2025年第2期68-76,共9页Chinese Journal of Ship Research

基　　金：国家自然科学基金资助项目(52272367)。

摘　　要：[目的]针对监测信号在单一分析域内的特征参数难以完整表征监测对象的运行状态,以及极限学习机(ELM)网络的模型参数难以达到最优的问题,提出一种基于多域信息融合与改进ELM的船舶电机轴承故障诊断方法。[方法]首先,基于船舶电机轴承振动信号在时域、频域与时频域内的特征信息,构建多域特征参数集,作为故障诊断模型的输入;然后,运用麻雀搜索算法改进ELM网络的模型参数优化方法,确定最优的权值与阈值,进而提高故障诊断ELM模型的识别精度。最后,通过船用电机试验台架实验数据和开源实验数据,对电机轴承故障状态进行识别。[结果]基于船用电机试验台架的实验数据验证表明,采用多域特征参数集的故障诊断模型在训练集和测试集上的识别精度均为100%;基于开源实验数据验证表明,改进ELM模型的测试集识别精度为90.5%,相较于原始ELM模型提高了12.7%,且训练集识别精度与测试集识别精度均高于其他诊断模型。[结论]所提方法在输入特征参数集与诊断模型上均有改进,可有效识别电机轴承故障状态,且模型具有良好的稳定性,为船舶电机轴承故障诊断提供参考。[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 improved 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 experimental 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 multidomain 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 proposed 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 

分 类 号：U664.21[交通运输工程—船舶及航道工程]