Infrared thermography provides abundant spatiotemporal degradation information, facilitating non-contact condition monitoring. Reducing domain shift between simulated and industrial infrared images is significantly desired for leveraging labeled simulated data to tackle practical insufficiency of run-to-failure samples. Recently, adversarial-based domain adaptation (DA) techniques have aroused broad concern in solving domain shifts. However, simultaneously aligning marginal and conditional distributions in cross-domain remaining useful life (RUL) prediction is rarely researched in adversarial-based DA. In this article, an adversarial regressive domain adaptation (ARDA) approach is, thus, put forward to address this challenge. First, a regressive disparity discrepancy is designed to describe the dissimilarity between distributions and derive the generalization bound for cross-domain prognostics. Guided by this bound, the ARDA effectively aligns marginal and conditional distributions by learning indistinguishable features and considering the relationship between samples and prediction tasks. Simulated and experimental infrared degradation image datasets are used to demonstrate the effectiveness and superiority of the proposed approach over existing methods for cross-domain RUL prediction.

中文翻译：

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基于红外热成像的无监督剩余使用寿命预测的对抗性回归域自适应方法

红外热成像提供丰富的时空退化信息，有助于非接触式状态监测。减少模拟和工业红外图像之间的域偏移对于利用标记的模拟数据来解决运行失败样本的实际不足是非常需要的。最近，基于对抗的域适应（DA）技术在解决域转移方面引起了广泛关注。然而，在基于对抗的 DA 中，很少研究同时对齐跨域剩余使用寿命 (RUL) 预测中的边际分布和条件分布。因此，在本文中，提出了一种对抗性回归域适应 (ARDA) 方法来应对这一挑战。第一的，回归差异差异旨在描述分布之间的差异并得出跨域预测的泛化界限。在这个界限的指导下，ARDA 通过学习不可区分的特征并考虑样本和预测任务之间的关系，有效地对齐边缘分布和条件分布。模拟和实验红外退化图像数据集用于证明所提出的方法相对于现有跨域 RUL 预测方法的有效性和优越性。