Rolling bearings are the most critical parts of rotating machinery and their damage is the leading cause of system failures. To ensure the reliability of the system, it demands to construct a health indicator (HI) to assess the state of degradation. However, existing HI construction methods (HICMs) have two limitations. First, the integration of well-designed features relies heavily on the experience of domain expert knowledge. Second, the construction of intelligent HI relies too much on life-cycle data. To cope with these limitations, this article proposed an HICM–Wasserstein dual-domain adversarial networks (WD-DAN), namely HICM-WD-DAN, which can extract generalized features with only normal data during the training. The dual-domain restriction of regularization promotes the generated signals approach to normal samples, making the constructed HI more robust and accurate. Moreover, to balance the weights of dual-domain parts automatically, an independent weighting structure is introduced. Finally, considering the actual degradation state of the system, the modified monotonicity and trendability indexes are proposed to evaluate the performance of HI. The effectiveness of HICM-WD-DAN is verified by bearings’ life-cycle data, and the results show that the constructed HI can represent the irreversible degradation process of bearings accurately and monotonously.

中文翻译：

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仅正常数据下基于Wasserstein双域对抗网络的轴承健康指标构建方法


滚动轴承是旋转机械最关键的部件，其损坏是系统故障的主要原因。为了保证系统的可靠性，需要构建健康指标（HI）来评估退化状态。然而，现有的 HI 构建方法（HICM）有两个局限性。首先，精心设计的功能的集成在很大程度上依赖于领域专家知识的经验。其次，智能HI建设​​过于依赖全生命周期数据。为了应对这些限制，本文提出了一种HICM-Wasserstein双域对抗网络（WD-DAN），即HICM-WD-DAN，它可以在训练过程中仅用正常数据提取广义特征。正则化的双域限制促进生成的信号接近正常样本，使得构建的HI更加稳健和准确。此外，为了自动平衡双域部分的权重，引入了独立的加权结构。最后，考虑到系统的实际退化状态，提出改进的单调性和趋势性指标来评估HI的性能。通过轴承的生命周期数据验证了HICM-WD-DAN的有效性，结果表明所构造的HI能够准确、单调地表示轴承的不可逆退化过程。