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Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis

机译：用于轴承故障诊断的三重损失引导对抗域自适应

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摘要

Recently, deep learning methods are becomingincreasingly popular in the field of fault diagnosis and achieve great success. However, since the rotation speeds and load conditions of rotating machines are subject to change during operations, the distribution of labeled training dataset for intelligent fault diagnosis model is different from the distribution of unlabeled testing dataset, where domain shift occurs. The performance of the fault diagnosis may significantly degrade due to this domain shift problem. Unsupervised domain adaptation has been proposed to alleviate this problem by aligning the distribution between labeled source domain and unlabeled target domain. In this paper, we propose triplet loss guided adversarial domain adaptation method (TLADA) for bearing fault diagnosis by jointly aligning the data-level and class-level distribution. Data-level alignment is achieved using Wasserstein distance-based adversarial approach, and the discrepancy of distributions in feature space is further minimized at class level by the triplet loss. Unlike other center loss-based class-level alignment approaches, which hasto compute the class centers for each class and minimize the distance of same class center from different domain, the proposed TLADA method concatenates 2 mini-batches from source and target domain into a single mini-batch and imposes triplet loss to the whole mini-batch ignoring the domains. Therefore, the overhead of updating the class center is eliminated. The effectiveness of the proposed method is validated on CWRU dataset and Paderborn dataset through extensive transfer fault diagnosis experiments.
机译：近年来，深度学习方法在故障诊断领域变得越来越流行，并取得了巨大的成功。但是，由于旋转机械的转速和负载条件在运行过程中会发生变化，因此用于智能故障诊断模型的标记训练数据集的分布与发生域偏移的未标记测试数据集的分布不同。由于此域转移问题，故障诊断的性能可能会大大降低。已经提出了无监督域自适应以通过对齐标记的源域和未标记的目标域之间的分布来缓解此问题。在本文中，我们通过联合对齐数据级别和类别级别的分布，提出了三重损失指导对抗域自适应方法（TLADA）用于轴承故障诊断。使用基于Wasserstein距离的对抗方法可以实现数据级别的对齐，并且由于三元组损失，在类级别进一步减少了特征空间中分布的差异。与其他基于中心损失的类级别对齐方法不同，该方法必须为每个类计算类中心，并最小化同一类中心到不同域的距离，而所提倡的TLADA方法将源域和目标域的2个小批次连接到一个mini-batch，对整个mini-batch施加了三重态丢失，而忽略了域。因此，消除了更新班级中心的开销。通过广泛的传输故障诊断实验，在CWRU数据集和Paderborn数据集上验证了该方法的有效性。

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