基于迁移学习与加权多通道融合的齿轮箱故障诊断

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摘要针对齿轮箱单一传感器故障识别精度波动大、数据利用率低、可靠性低及故障诊断模型在多工况下泛化能力不足等问题，提出了一种加权融合多通道数据与深度迁移模型的齿轮箱故障诊断方法。首先，为了充分挖掘齿轮箱多通道数据的信息，提出了基于信息熵加权的多通道融合方法，采用信息熵法计算各通道数据的融合权重，并对各通道的采样数据进行加权融合。其次，利用源域的融合数据对深度迁移模型进行预训练，将预训练得到的模型参数作为目标域模型的初始化参数，同时冻结目标域模型特征提取器的参数，并利用目标域的融合数据对目标域模型分类器的参数进行微调，实现深度迁移模型从源域到目标域的迁移以适应新的目标样本识别任务。最后，齿轮箱多工况迁移诊断试验结果表明，所提方法可有效用于齿轮箱的故障诊断，相比传统迁移学习方法BDA、TCA、JDA、JGSA、GFK及深度迁移学习方法AdaBN、MK-MMD、DCTLN这8种当前常用方法，具有更高的平均迁移诊断精度和变工况下良好的泛化性能。

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	侯召国
	王华伟
	熊明兰
	王峻洲

关键词 ：故障诊断, 齿轮箱, 深度迁移模型, 加权多通道融合, 多工况

Abstract：A gearbox fault diagnosis method based on weighted fusion of multi-channel data and deep transfer model was proposed to solve the problems of large fluctuation of fault identification accuracy of single sensor, low data utilization, low reliability and insufficient generalization ability of fault diagnosis model under multiple working conditions. Firstly, in order to fully mine the information of multi-channel data of gearbox, a multi-channel fusion method based on information entropy weighting is proposed. The fusion weight of data of each channel is calculated by using information entropy method, and the sampled data of each channel is weighted fused. Secondly, the deep transfer model is pre-trained by using the fusion data of source domain, and the model parameters obtained by the pre-training are used as the initialization parameters of the target domain model. Meanwhile, the parameters of the feature extractor of target domain model are frozen, and the parameters of the classifier of target domain model are fine-tuned by using the fusion data of target domain. In order to adapt to the new target sample recognition task, the deep transfer model is transferred from source domain to target domain. Finally, the multi-condition transfer diagnosis test results of gearbox show that the proposed method can be effectively used for gearbox fault diagnosis. Compared with the traditional transfer learning methods BDA, TCA, JDA, JGSA, GFK and the deep transfer learning methods AdaBN, MK-MMD, DCTLN, It has higher average transfer diagnosis accuracy and better generalization performance under variable working conditions.

Key words： Fault diagnosis Gearbox Deep transfer model Weighted multi-channel fusion Many conditions

收稿日期: 2021-12-22 出版日期: 2023-05-15

引用本文:

侯召国，王华伟，熊明兰，王峻洲. 基于迁移学习与加权多通道融合的齿轮箱故障诊断[J]. 振动与冲击, 2023, 42(9): 236-246.
HOU Zhaoguo, WANG Huawei, XIONG Minglan, WANG Junzhou. Gearbox fault diagnosis based on transfer learning and weighted multi-channel fusion. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(9): 236-246.

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http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I9/236

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