Xu Zhiqing, Chow Christopher W K, Rahman Md Mizanur, Rameezdeen Raufdeen, Law Yee Wei
Sustainable Infrastructure and Resource Management (SIRM), UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia.
Sensors (Basel). 2025 Jul 22;25(15):4536. doi: 10.3390/s25154536.
In recent years, domain adaptation (DA) has been extensively applied to predicting the remaining useful life (RUL) of bearings across conditions. Although traditional DA-based methods have achieved accurate predictions, most methods fail to extract multi-scale degradation information, focus only on global-scale DA, and ignore the importance of temporal weights. These limitations hinder further improvements in prediction accuracy. This paper proposes a novel model, called the health indicator-weighted subdomain alignment network (HIWSAN), which first learns feature representations at multiple scales, then constructs health indicators as temporal weights, and finally performs subdomain-level alignment. Two case studies based on the XJTU-SY and PRONOSTIA datasets were conducted, covering ablation, comparison, and generalization experiments to evaluate the proposed HIWSAN. Experimental results show that HIWSAN achieves an average MAE of 0.0989 and an average RMSE of 0.1189 across two datasets, representing reductions of 21.07% and 25.13%, respectively, compared to existing state-of-the-art methods.
近年来，域适应（DA）已被广泛应用于跨工况预测轴承的剩余使用寿命（RUL）。尽管基于传统DA的方法已实现了准确预测，但大多数方法未能提取多尺度退化信息，仅关注全局尺度的DA，且忽略了时间权重的重要性。这些局限性阻碍了预测精度的进一步提高。本文提出了一种名为健康指标加权子域对齐网络（HIWSAN）的新型模型，该模型首先在多个尺度上学习特征表示，然后将健康指标构建为时间权重，最后进行子域级对齐。基于XJTU-SY和PRONOSTIA数据集进行了两个案例研究，涵盖消融、比较和泛化实验，以评估所提出的HIWSAN。实验结果表明，HIWSAN在两个数据集上的平均MAE为0.0989，平均RMSE为0.1189，与现有最先进方法相比，分别降低了21.07%和25.13%。
