基于MPDCNN的强噪声环境下船舶电力推进器齿轮箱故障诊断方法  

Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments

作　　者：尚前明[1] 蒋婉莹 周毅[1] 王正强 孙钰波 SHANG Qianming;JIANG Wanying;ZHOU Yi;WANG Zhengqiang;SUN Yubo(School of Naval Architecture,Ocean and Energy Power Engineering,Wuhan University of Technology,Wuhan 430063,China)

机构地区：[1]武汉理工大学船海与能源动力工程学院,湖北武汉430063

出　　处：《中国舰船研究》2025年第2期30-38,共9页Chinese Journal of Ship Research

基　　金：国家重点研发计划项目(2019YFE0104600);国家自然科学基金资助项目(51909200)。

摘　　要：[目的]针对旋转机械在实际工作中因噪声干扰而导致的故障诊断性能下降问题,为提高振动信号的故障特征提取质量和故障诊断能力,提出基于Mel-frequency倒谱系数(MFCC)的并行双通道卷积神经网络(PDCNN)故障诊断方法。[方法]利用MFCC提取含噪声的振动信号特征,同时设计一种新型并行双通道卷积神经网络结构,并利用该网络进一步挖掘数据的全局特征及更深层次的微小特征,从而提高该方法在强噪声环境下的诊断性能。[结果]不同噪声环境下的实验评估结果表明,该方法在强噪声环境下的故障诊断精度高于98%,其抗噪性能和诊断性能均明显优于其他传统方法。[结论]研究成果可为强噪声环境下的齿轮箱故障诊断提供参考。[Objectives]To address the performance degradation in fault diagnosis of rotating machinery caused by noise interference in practical applications,a novel fault diagnosis approach based on Melfrequency cepstral coefficients(MFCC)and a parallel dual-channel convolutional neural network(PDCNN)is proposed.This method aims to improve the quality of fault feature extraction from vibration signals and enhance fault diagnosis capabilities under noisy conditions.[Methods]The MFCC is used to extract features from vibration signals contaminated by noise.Meanwhile,a novel parallel dual-channel convolutional neural network structure is designed to explore both global features and deeper,finer details of the data,thereby enhancing the diagnostic performance of the method in strong noise environments.[Results]Experimental evaluation results under different noise conditions show that the proposed method achieves a fault diagnosis accuracy of over 98%in environments with strong noise.Its robustness to noise and diagnostic performance significantly surpass traditional methods.[Conclusion]The findings of this study can provide valuable references for gearbox fault diagnosis in environments with strong noise.

关 键 词：船舶电力推进 齿轮箱 故障分析 故障诊断 特征提取 梅尔频率倒谱系数 卷积神经网络 

分 类 号：U664.21[交通运输工程—船舶及航道工程]