中国舰船研究2025，Vol.20Issue(2)：20-29,10.DOI:10.19693/j.issn.1673-3185.04059
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
摘要
Abstract
[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 diagnos-is 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 meth-od,the parameters are built to simulate the interference of synthetic noise on the model training,and the net-work is guided by the Residual Efficient Channel Attention(RECA)module to extract the fault difference fea-tures.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 sig-nal-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)Key words
electric motors/failure analysis/fault diagnosis/artificial intelligence/complementary ensemble empirical mode decomposition with adaptive noise/Bayesian residual efficient channel attention network分类
交通工程引用本文复制引用
朱仁杰,宋恩哲,姚崇,柯赟..强噪声背景下基于CEEMDAN与BRECAN的船舶电机故障诊断[J].中国舰船研究,2025,20(2):20-29,10.基金项目
山东省自然科学基金资助项目(ZR2023QE009) （ZR2023QE009）
中央高校基本科研业务费专项资金资助项目(3072024XX2709) （3072024XX2709）
内燃机与动力系统全国重点实验室开放课题(skler-2023-011) （skler-2023-011）
