AFARN: Domain Adaptation for Intelligent Cross-Domain Bearing Fault Diagnosis in Nuclear Circulating Water Pump
AFARN: Domain Adaptation for Intelligent Cross-Domain Bearing Fault Diagnosis in Nuclear Circulating Water Pump
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DOI：
10.1109/tii.2022.3177459
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发表时间：
2023-03
影响因子：
12.3
通讯作者：
Wei Cheng-;Xue Liu;Ji Xing;Xuefeng Chen;Baoqing Ding;Rongyong Zhang;Kangning Zhou;Qian Huang
中科院分区：
文献类型：
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作者：
Wei Cheng-;Xue Liu;Ji Xing;Xuefeng Chen;Baoqing Ding;Rongyong Zhang;Kangning Zhou;Qian Huang
领域适应可以通过最小化标记源数据和未标记目标数据的分歧来传输跨域诊断知识。然而，该模型在特征提取和分布对齐过程中忽略了最大化物理先验知识，导致模型不可解释甚至负迁移。因此，提出了一种物理信息域自适应网络，称为自适应故障注意残差网络（AFARN）。首先，设计了自适应故障注意机制来细化由轴承故障特征引导的特征，适合生成与诊断相关的特征。然后，应用几个指标来最小化特征的边际和条件分布差异，从而将模型从源域推广到目标域。 AFARN同时利用故障特征和标签信息来训练模型，可以增强诊断相关特征的分布对齐，从而提供可解释的知识转移。最后，在公共和循环水泵数据集上的实验表明，AFARN可以提高故障特征学习和诊断的准确性。
Domain adaptation can transfer cross-domain diagnosis knowledge by minimizing the divergence of labeled source and unlabeled target data. However, the model neglects to maximize physics prior knowledge during feature extraction and distribution alignment, resulting in a noninterpretable model even negative transfer. Hence, a physics-informed domain adaptation network, termed adaptive fault attention residual network (AFARN), is proposed. First, an adaptive fault attention mechanism is designed to refine features guided by bearing fault characteristics, suited to generating diagnosis-relevant features. Then, several metrics are applied to minimize the marginal and conditional distribution discrepancy of features, thus, generalizing the model from source to target domain. The AFARN utilizes the fault characteristics and label information simultaneously to train the model, which can enhance the distribution alignment of diagnosis-relevant features, thus, providing an interpretable knowledge transfer. Finally, experiments on public and circulating water pump datasets show that AFARN can enhance fault feature learning and diagnosis accuracy.