[1]李 斌,张 砦,李泓锟.基于 MSAWPD-CNN 的行星齿轮箱端到端故障诊断研究[J].机械与电子,2025,(07):74-80.
　LI Bin,ZHANG Zhai,LI Hongkun.Research on End-to-end Fault Diagnosis of Planetary Gearbox Utilizing MSAWPD-CNN[J].Machinery & Electronics,2025,(07):74-80.
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基于 MSAWPD-CNN 的行星齿轮箱端到端故障诊断研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年07期
页码:
74-80
栏目:
机电一体化
出版日期:
2025-07-27

文章信息/Info

Title:
Research on End-to-end Fault Diagnosis of Planetary Gearbox Utilizing MSAWPD-CNN
文章编号:
1001-2257 ( 2025 ) 07-0074-07
作者:
李 斌; 张 砦; 李泓锟
南京航空航天大学自动化学院,江苏 南京 211106
Author(s):
LI Bin ;  ZHANG Zhai ;  LI Hongkun
( College of Automation Engineering , Nanjing University of Aeronautics and Astronautics , Nanjing 211106 , China )
关键词:
行星齿轮箱; 故障诊断; 端到端; 自适应小波包分解; 卷积神经网络
Keywords:
lanetary gearbox ;  fault diagnosis ;  end to end ;  adaptive wavelet packet decomposition ;  convolutional neural network
分类号:
TH132 ;TP18
文献标志码:
A
摘要:
针对传统行星齿轮箱故障诊断网络的人工特征提取困难的问题,提出了将小波包函数约束的自适应小波包分解( AWPD )卷积核融入 CNN 网络,构建行星齿轮箱端到端故障诊断网络 MSAWPD-CNN 。首先将 2 种频率尺度的 AWPD 卷积核融入到诊断网络的第 1 层,使诊断网络聚焦于可以有效表达行星齿轮箱故障的时频特征;其次将 2 种频率尺度的时频特征分别通过 2 层 1D 卷积进行特征压缩;最后将压缩后的特征堆叠送入骨干 CNN 进行深层特征提取与故障模式分类。实验结果表明,所提出的网络可以有效聚焦时频特征,加速训练结果的收敛,并且在测试集中诊断准确率最高。
Abstract:
Aiming at the difficulty of artificial feature extraction in traditional planetary gearbox fault diagnosis network , an end to end planetary gearbox fault diagnosis network MSAWPD CNN is constructed by integrating the convolution kernel of adaptive wavelet packet decomposition ( AWPD ) constrained by wavelet packet function into CNN network.Firstly , the AWPD convolution kernels of two frequency scales are incorporated into the first layer of the diagnostic network , enabling the diagnostic network to concentrate on the time frequency features that can effectively represent the faults of planetary gearboxes ; secondly , the time-frequency features of the two frequency scales are respectively compressed via two layers of 1D convolution ; finally , the compressed features are stacked and delivered to the backbone CNN for deep feature extraction and fault pattern classification.The experimental results indicate that the network proposed in this paper can effectively focus on the time-frequency features , expedite the convergence of the training results , and achieve the highest diagnostic accuracy in the test set.

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备注/Memo

备注/Memo:
收稿日期: 2024-11-27
作者简介:李 斌 ( 1996- ),男,江苏宿迁人,硕士研究生,研究方向为行星齿轮箱故障诊断;张 砦 ( 1980- ),男,安徽歙县人,博士,副教授,研究方向为航天器测试、智能故障诊断和数字系统容错,通信作者, E-mail : wolnyzhang@nuaa.edu.cn ;李泓锟( 2000- ),男,四川绵阳人,硕士研究生,研究方向为行星齿轮箱故障诊断。
更新日期/Last Update: 2025-09-02