Multichannel Information Fusion and Deep Transfer Learning for Rotating Machinery Fault Diagnosis

doi:10.3969/j.issn.1004-132X.2023.08.011

Abstract

Abstract: To address the problems of inadequate information on the characteristics of single channel signals， a rotating machinery fault diagnosis method of multichannel information fusion and deep transfer learning approach was proposed. Firstly， the one-dimensional signals collected by multi-sensors were used to generate separate time-frequency maps by wavelet transforms. Then， the information of the maps was fused into multi-channel images. Finally， the pre-trained deep residual network， as a transfer model， was used for fault diagnosis of the rotating machinery. The identification accuracy of the tests on the cylindrical roller bearing， locomotive bearing， and gearbox datasets of a bureau of locomotive section is as 99.23%， 99.78%， and 99.50% respectively； and the identification accuracy of the cross-service transfer tests on the Case Western Reserve University bearing dataset is as 93.12%， which indicates the superiority and scalability of the proposed method.

Key words: fault diagnosis, rotating machinery, multichannel information fusion, deep transfer learning

摘要： 针对单通道信号特征信息不充分的问题，提出了一种多通道信息融合与深度迁移学习的旋转机械故障诊断方法。首先使用小波变换将多传感器采集的一维信号生成多幅时频图，然后将时频图信息融合为多通道图像，最后将预训练的深度残差网络作为迁移模型对旋转机械进行故障诊断。圆柱滚子轴承、某局机务段机车轴承和齿轮箱数据集的识别准确率分别为99.23%、99.78%和99.50%，凯斯西储大学轴承数据集的跨工况迁移试验识别准确率达93.12%，这表明所提方法具有一定的优越性和可扩展性。

关键词: 故障诊断, 旋转机械, 多通道信息融合, 深度迁移学习

CLC Number:

TH13
TH17

ZHANG Long, HU Yanqing, ZHAO Lijuan, ZHANG Hao. Multichannel Information Fusion and Deep Transfer Learning for Rotating Machinery Fault Diagnosis[J]. China Mechanical Engineering, 2023, 34(08): 966-975.

张龙, 胡燕青, 赵丽娟, 张号. 多通道信息融合与深度迁移学习的旋转机械故障诊断[J]. 中国机械工程, 2023, 34(08): 966-975.

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