Self-supervised learning-based dual-classifier domain adaptation model for rolling bearings cross-domain fault diagnosis
Self-supervised learning-based dual-classifier domain adaptation model for rolling bearings cross-domain fault diagnosis
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DOI：
10.1016/j.knosys.2023.111229
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
Q. Jiang;Xiaoshan Lin;Xingchi Lu;Yehu Shen;Qixin Zhu;Qingkui Zhang
中科院分区：
文献类型：
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作者：
Q. Jiang;Xiaoshan Lin;Xingchi Lu;Yehu Shen;Qixin Zhu;Qingkui Zhang
双分类器领域自适应方法侧重于两个分类器之间的输出一致性，而忽略了每个分类器的分类确定性，降低了模型的故障识别能力。为了解决这一问题，提出了一种基于自监督学习的双分类器域自适应模型（SLDDA）用于轴承的跨域故障诊断。首先，为了缓解分类器之间的输出歧义，建立了双分类器分类确定性度量，该度量同时考虑了两个分类器之间的联合确定性和每个分类器的单独确定性；其次，提出了一种基于目标数据聚类的自监督学习方法，从目标数据中提取丰富的目标特征；在帕德博恩大学（PU）和H0205轴承数据集上进行了两次验证实验。与最大分类器差异方法相比，所提出的SLDDA方法将PU数据集的诊断准确率平均提高了14 %，有效地实现了变工况下滚动轴承的跨域故障诊断。
The dual-classifier domain adaptation methods concentrate on the output consistency between two classifiers, while ignoring the classification determinacy of each classifier and reducing the fault identification capability of the model. To address this challenge, a self-supervised learning-based dual-classifier domain adaptation model (SLDDA) is presented for cross-domain fault diagnosis of bearings. Firstly, a dual-classifier classification determinacy metric is formulated to alleviate the output ambiguity between classifiers, which simultaneously considers the joint determinacy between two classifiers along with the individual determinacy of each classifier. Secondly, a self-supervised learning approach based on the clustering of target data is proposed to extract abundant target features from the target data. Furthermore, two validation experiments are conducted on bearing datasets of Paderborn University (PU) and H0205. Comparing with the Maximum Classifier Discrepancy method, the proposed SLDDA improves the diagnostic accuracy by an average of 14 % of the PU dataset, which efficiently fulfills the cross-domain fault diagnosis of rolling bearings under variable operating conditions.