深度领域自适应及其在跨工况故障诊断中的应用

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摘要实际生产中，机械设备的工况变化会造成监测数据的分布差异，破坏分类模型的应用基础，降低诊断准确率。为此，提出一种基于深度学习的领域自适应方法，用于跨工况情境下轴承故障诊断。该方法构建两个级联的深度网络：前者用于处理振动信号，自动挖掘故障敏感特征；后者用于将不同工况的样本特征同步映射到一个深度隐藏层（公共特征空间）中，消除工况波动引起的分布差异，生成工况不变特征，实现领域自适应。此外，该深度映射网络可通过参数优化方法自适应构建，能够实现最佳的跨域诊断性能。实验表明，与其他方法和相关研究相比，深度领域自适应在跨工况故障识别中具有更高的准确率。

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	袁壮1
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	董瑞2
	张来斌1
	段礼祥1

关键词 ：深度学习, 领域自适应, 变工况, 故障诊断

Abstract：In the real-world production, variable working conditions lead to the distribution discrepancy in the monitoring data, which undermines the basis of classification models and consequently deteriorates the diagnosis accuracy. Therefore, a deep learning-based domain adaptation method is proposed for rolling bearing fault diagnosis across working conditions. In this method, two deep networks are constructed in a cascaded way. The first one is applied to process vibration signals and automatically mine fault-sensitive features. The second one maps sample features from different working conditions onto a deep hidden layer (the common feature space) to reduce the distribution discrepancy caused by fluctuant working conditions, generate working-condition-independent features, and realize domain adaptation. Moreover, this deep projection network is adaptively established by parameter optimization for the best cross-domain diagnosis performance. The experiments show that, compared with peer approaches and related literatures, deep domain adaptation has a higher accuracy in fault recognition across working conditions.

Key words： deep learning domain adaptation variable working condition fault diagnosis

收稿日期: 2019-01-10 出版日期: 2020-06-15

引用本文:

袁壮1,2，董瑞2，张来斌1，段礼祥1. 深度领域自适应及其在跨工况故障诊断中的应用[J]. 振动与冲击, 2020, 39(12): 281-288.
YUAN Zhuang1,2, DONG Rui2, ZHANG Laibin1, DUAN Lixiang1. Deep domain adaptation and its application in fault diagnosis across working conditions. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(12): 281-288.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I12/281

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