中国舰船研究2025，Vol.20Issue(2)：30-38,9.DOI:10.19693/j.issn.1673-3185.03814
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
摘要
Abstract
[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 Mel-frequency 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 en-hance 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 en-hancing 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 ref-erences 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Key words
electric propulsion/gearboxes/failure analysis/fault diagnosis/feature extraction/Mel-fre-quency cepstral coefficients/convolutional neural networks分类
交通工程引用本文复制引用
尚前明,蒋婉莹,周毅,王正强,孙钰波..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[J].中国舰船研究,2025,20(2):30-38,9.基金项目
国家重点研发计划项目(2019YFE0104600) （2019YFE0104600）
国家自然科学基金资助项目(51909200) （51909200）
