强噪声背景下基于CEEMDAN与BRECAN的船舶电机故障诊断  

Marine motor fault diagnosis based on CEEMDAN and BRECAN under strong noise conditions

作　　者：朱仁杰 宋恩哲 姚崇 柯赟 ZHU Renjie;SONG Enzhe;YAO Chong;KE Yun(College of Power and Energy Engineering,Harbin Engineering University,Harbin 150001,China;Yantai Research Institute,Harbin Engineering University,Harbin 264000,China)

机构地区：[1]哈尔滨工程大学动力与能源工程学院,黑龙江哈尔滨150001 [2]哈尔滨工程大学烟台研究院,山东烟台264000

出　　处：《中国舰船研究》2025年第2期20-29,共10页Chinese Journal of Ship Research

基　　金：山东省自然科学基金资助项目(ZR2023QE009);中央高校基本科研业务费专项资金资助项目(3072024XX2709);内燃机与动力系统全国重点实验室开放课题(skler-2023-011)。

摘　　要：[目的]针对船舶航行中机舱背景噪声导致故障诊断方法在实际使用时精度差的问题,提出一种基于自适应噪声的完备经验模态分解(CEEMDAN)和贝叶斯残差高效通道注意力网络(BRECAN)的船舶电机故障诊断方法。[方法]首先,通过CEEMDAN将含噪声电机故障信号分解为多个本征模态函数(IMF)分量,并基于去趋势波动分析(DFA)划分IMF中噪声和信息的主导信号,对于噪声主导信号使用经验小波变化(EWT)予以降噪;然后,构建BRECAN网络,基于变分贝叶斯理论,使用网络参数代替传统网络点估计的训练方式,使用参数建模,拟合噪声对模型训练的干扰,并通过残差高效通道注意力(RECA)模块引导网络提取故障差异特征;最后,通过电机故障模拟实验台,验证所提方法的有效性。[结果]结果表明,所提方法在强噪声下能够实现船舶电机故障的精确诊断,在信噪比为-12dB的条件下仍能保持90%以上的诊断精度。[结论]研究成果可为强噪声下船舶电机故障诊断提供参考。[Objective]The background noise in the engine room during actual ship navigation leads to the poor accuracy in fault diagnosis methods.To address this issue,this paper proposes a ship motor fault diagnosis method based on complementary ensemble empirical mode decomposition(EEMD)with adaptive noise(CEEMDAN)and a Bayesian residual efficient channel attention network(BRECAN).[Methods]First,the noisy motor fault signal is decomposed into multiple intrinsic mode components(IMFs)through adaptive noise CEEMDAN,the noise dominant signal and information dominant signal in the IMF are divided on the basis of detrended fluctuation analysis,and empirical wavelet transform(EWT)is used to de-noise the noise dominant signal.Next,the BRECAN network is constructed,based on the principle of Variational Bayesian(VI-Bayesian)using the network parameters instead of the traditional network point estimation training method,the parameters are built to simulate the interference of synthetic noise on the model training,and the network is guided by the Residual Efficient Channel Attention(RECA)module to extract the fault difference features.Finally,the effectiveness of the method is verified via a motor fault simulation experimental platform.[Results]The results show that the proposed method can achieve the accurate diagnosis of ship motor faults under strong noise conditions while still maintaining a diagnostic accuracy of over 90%under signal-to-noise ratio of−12 dB.[Conclusion]The results of this study can provide valuable references for the diagnosis of ship motor faults under strong noise conditions.

关 键 词：电动机 故障分析 故障诊断 人工智能 完全集合经验模态分解(CEEMDAN) 贝叶斯残差高效通道注意力网络(BRECAN) 

分 类 号：U672.7[交通运输工程—船舶及航道工程] U664.14[交通运输工程—船舶与海洋工程]