谱归一化CycleGAN的轴承故障迁移诊断研究

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摘要深度学习无需先验特征提取的优点使其受到了工业设备的智能故障诊断领域研究的青睐，但深度学习的低鲁棒性和较高的数据要求阻碍其实际应用。为适应在工业现场复杂多变的工况，本文提出了一种基于谱归一化(spectral normalization，SN)和循环一致对抗网络(cycle-consistent adversarial networks，CycleGAN)的SN_1DCycleGAN网络用于变工况条件下的故障数据迁移生成和诊断。首先，搭建一种适应振动数据生成的1DCycleGAN网络，用于获得同种工况下正常信号与故障信号的映射关系。使用谱归一化对网络进行改进，有效的防止CycleGAN网络训练过程中训练不稳定情况。其次，通过不同工况的正常数据生成自适应的故障数据，实现变工况迁移生成的目的。最后，三种评价指标以及分类器准确率对数据生成质量进行定量评估，并使用仿真与实验信号进行验证。实验结果表明，SN_1DCycleGAN在一维振动信号上具备一定迁移效果，可对变工况数据进行增强，提升分类器的准确率。

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	李洁松1
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	刘韬1
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关键词 ：智能故障诊断, 循环一致对抗网络, 谱归一化, 变工况迁移生成

Abstract：The advantage of deep learning without prior feature extraction makes it increasingly used in intelligent fault diagnosis of industrial equipment, but the low robustness and high data requirements of deep learning methods hinder practical applications. In order to adapt to the complex and variable working conditions in industrial sites, this paper proposes a SN_1DCycleGAN network based on spectral normalization (SN) and cycle-consistent adversarial networks (CycleGAN) for fault data transfer generation and diagnosis under variable working conditions. Firstly, a 1DCycleGAN network adapted to vibrational data generation is built for obtaining the mapping relationship between the source and target domains. The network is improved using spectral normalization to effectively prevent the training instability situation. Secondly, adaptive target data are obtained by changing the source domain to achieve the purpose of variable work transfer. Finally, the quality of data generation is quantitatively evaluated using three evaluation metrics as well as classifier accuracy, and validated using simulation and experimental signals. The experimental results show that SN_1DCycleGAN has a certain transfer effect on 1D vibration signals, which can enhance the variable working condition data and improve the accuracy of the classifier. The stability and generation quality are better than 1DCycleGAN.

Key words： intelligent fault diagnosis cycle-consistent adversarial networks spectral normalization varia ble condition transfer generation

收稿日期: 2022-09-05 出版日期: 2023-12-28

引用本文:

李洁松1,2，刘韬1,2，伍星2,3. 谱归一化CycleGAN的轴承故障迁移诊断研究[J]. 振动与冲击, 2023, 42(24): 282-289.
LI Jiesong1,2， LIU Tao1,2， WU Xing2,3. Spectral normalization CycleGAN for bearing fault transfer diagnosis. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(24): 282-289.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I24/282

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