[1]周旭涛,杨泽昀,吴文开.基于多通道并行LSTM-CNN的旋转机械故障诊断[J].工业仪表与自动化装置,2025,(02):92-98.[doi:10.19950/j.cnki.CN61-1121/TH.2025.02.017]
　ZHUO Xutao,YANG Zeyun,WU Wenkai.Fault diagnosis of rotating machinery based on multi-channel parallel LSTM-CNN[J].Industrial Instrumentation & Automation,2025,(02):92-98.[doi:10.19950/j.cnki.CN61-1121/TH.2025.02.017]

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基于多通道并行LSTM-CNN的旋转机械故障诊断(PDF)

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:

期数:

2025年02期

页码:

92-98

栏目:

出版日期:

2025-04-15

文章信息/Info

Title:

Fault diagnosis of rotating machinery based on multi-channel parallel LSTM-CNN

文章编号:

1000-0682（2025）02-0092-07

作者:

周旭涛; 杨泽昀; 吴文开

（1.中铁工程机械研究设计院有限公司, 湖北 武汉 430066；2.西安电子科技大学杭州研究院, 浙江 杭州 311231；3.中国船舶及海洋工程设计研究院, 上海 200011）

Author(s):

ZHUO Xutao;  YANG Zeyun;  WU Wenkai

(1. China Railway Engineering Machinery Research & Design Institute Co., Ltd., Hubei Wuhan 430066, China; 2. Hangzhou Institute of Technology, Xidian University, Zhejiang Hangzhou 311231, China; 3. Marine Design & Research Institute of China, Shanghai 200011, China)

关键词:

长短期记忆网络; 卷积神经网络; 旋转机械; 滚动轴承; 故障诊断

Keywords:

Long Short-Term Memory;  convolutional neural network;  rotating machinery;  rolling bearing;  fault diagnosis

分类号:

TH133.3 TH17

DOI:

10.19950/j.cnki.CN61-1121/TH.2025.02.017

文献标志码:

A

摘要:

针对旋转机械故障诊断信息特征提取不完整而造成的故障误判问题，提出一种以多通道信号作为输入、长短期记忆网络和卷积神经网络并行的旋转机械故障诊断模型。该模型以实际监测的多通道振动信号作为输入，分别捕获振动信号中的时间序列特征和局部空间特征，从而完成旋转机械故障诊断。采用凯斯西储大学轴承中心实验数据进行验证，实验结果表明，所提出的故障诊断模型训练收敛速度快、收敛精度高，且故障诊断准确率达99.99%以上。

Abstract:

Aiming at the problem of fault misjudgment caused by incomplete features extraction of rotating machinery fault diagnosis information, a rotating machinery fault diagnosis model based on multi-channel signal as input, Long Short-Term Memory(LSTM) network and Convolutional Neural Network(CNN) is proposed. The model takes the multi-channel vibration signal as the input, and captures the time series characteristics and local spatial characteristics of the vibration signal, so as to complete the fault diagnosis of rotating machinery. The experimental data from the bearing center of Case Western Reserve University are used to verify the proposed fault diagnosis model. The experimental results show that the proposed fault diagnosis model has fast convergence speed and high convergence accuracy, and the accuracy of fault diagnosis is more than 99.99%.

参考文献/References:

[1] 胡梦婷, 罗晨. 基于MCNN-LSTM和交叉熵损失函数的轴承故障诊断[J]. 制造技术与机床, 2024: 1–9.
[2] 王婧, 许志伟, 刘文静, 等. 滚动轴承健康智能监测和故障诊断机制研究综述[J]. 计算机科学与探索, 2024, 18(4): 878–898.
[3] He J, Li X, Chen Y, et al. Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis[J]. Shock and Vibration, 2021, 2021: 1–16.
[4] Lei J, Liu C, Jiang D. Fault diagnosis of wind turbine based on Long Short-term memory networks[J]. Renewable Energy, 2019, 133: 422–432.
[5] 李聪, 丁强, 刘亚祥, 等. 结合LSTM与1DCNN的冷水机组故障诊断方法研究[J]. 建筑科学, 2024, 40(6): 146–154+208.
[6] 徐行, 李军星, 贾现召, 等. 基于1DCNN-BiLSTM的端到端滚动轴承故障诊断方法[J]. 机床与液压, 2024, 52(11): 211–218.
[7] 景斯桐, 吴东升. 基于LSTM-CNN的双路径滚动轴承故障诊断[J]. 沈阳理工大学学报, 2024, 43(1): 44–49.
[8] 付雅婷, 温世明, 杨辉, 等. 基于多通道输入和1DCNN-LSTM的道岔转辙机故障诊断[J]. 铁道学报, 2023, 45(11): 98–106.
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备注/Memo

备注/Memo:

收稿日期：2024-07-23第一作者：周旭涛（1988—），男，汉族，山东荣成，硕士，工程师。研究方向为工程机械智能监测及安全评估技术。E-mail: zhouxutao@163.com

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