In an actual industrial environment, the complex working environment of mechanical equipment may lead to new faults in the target domain, called the open-set domain adaptation problem. Recently, open-set adaptive fault diagnosis has been extensively employed. However, most studies not only require pre-set fixed thresholds to identify unknown class features but also ignore the learning of discriminable features under specific tasks, which affects the diagnostic performance. Hence, this paper proposes a complementary weighted dual adversarial network combined with supervised contrastive learning (CWDAN-SCL) to address the open-set cross-different working fault diagnosis of bearings. Specifically, a novel complementary weighted adversarial learning strategy is designed using supervised classification and uncertainty measurement to effectively control the participation of target domain features in the domain adaptation process and achieve the alignment of shared class fault features between the source and target domains. Moreover, an adaptive unknown fault separation module is designed using an adversarial learning method to construct a hyperplane between shared and unknown class fault features in the target domain to identify unknown class faults accurately. Additionally, a supervised contrastive loss term is designed based on contrastive learning and label knowledge to improve the aggregation of fault features of the same class and enhance the model’s generalization ability in target domain diagnosis tasks. Subsequently, the efficacy and advancement of the proposed method are substantiated through experimentation on two datasets. The experimental results illustrate that the average diagnostic performance of the proposed method is 91.73 %. This study contributes a dependable diagnostic approach for ascertaining the health status of rotating machinery equipment.

中文翻译：

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基于监督对比学习和互补加权双对抗网络的开放域自适应故障诊断


在实际工业环境中，机械设备复杂的工作环境可能会导致目标域出现新的故障，称为开集域适应问题。近年来，开集自适应故障诊断已得到广泛应用。然而，大多数研究不仅需要预先设定的固定阈值来识别未知的类别特征，而且忽略了特定任务下可区分特征的学习，这影响了诊断性能。因此，本文提出了一种结合监督对比学习的互补加权对偶对抗网络（CWDAN-SCL）来解决轴承的开放集跨不同工作故障诊断问题。具体来说，利用监督分类和不确定性测量设计了一种新颖的互补加权对抗学习策略，以有效控制目标域特征在域适应过程中的参与，并实现源域和目标域之间共享类故障特征的对齐。此外，采用对抗学习方法设计了自适应未知故障分离模块，在目标域中的共享和未知类故障特征之间构造超平面，以准确识别未知类故障。此外，基于对比学习和标签知识，设计了有监督对比损失项，以提高同一类故障特征的聚合，增强模型在目标域诊断任务中的泛化能力。随后，通过对两个数据集的实验证实了所提出方法的有效性和先进性。实验结果表明，该方法的平均诊断率为91.73%。 这项研究为确定旋转机械设备的健康状况提供了可靠的诊断方法。