针对特定目标的适应与一致退化对齐用于跨域剩余寿命预测
Target-specific Adaptation and Consistent Degradation Alignment for Cross-Domain Remaining Useful Life Prediction
AI总结：
本文提出TACDA方法，通过目标域重建和一致退化对齐，提升跨域剩余寿命预测的准确性。
AI中文摘要：
准确预测机械设备的剩余寿命（RUL）可以显著降低维护成本，提高设备运行时间，并减轻不利后果。数据驱动的RUL预测技术已显示出出色的性能。然而，其效果往往依赖于训练和测试数据来自同一分布或领域的假设，这在实际工业环境中并不成立。为缓解这一领域差异问题，先前的对抗域适应方法专注于推导域不变特征。然而，它们忽略了目标特定信息和退化阶段的一致性特征，导致性能不佳。为解决这些问题，我们提出了一种新的域适应方法用于跨域RUL预测，称为TACDA。具体而言，我们提出了一种在对抗适应过程中针对目标域的重建策略，从而在学习域不变特征的同时保留目标特定信息。此外，我们开发了一种新的聚类和配对策略，用于相似退化阶段之间的一致对齐。通过广泛的实验，我们的结果证明了我们提出的方法TACDA的显著性能，超过了两种不同的评估指标下的最先进方法。我们的代码可在https://github.com/keyplay/TACDA上获得。
英文摘要：
Accurate prediction of the Remaining Useful Life (RUL) in machinery can significantly diminish maintenance costs, enhance equipment up-time, and mitigate adverse outcomes. Data-driven RUL prediction techniques have demonstrated commendable performance. However, their efficacy often relies on the assumption that training and testing data are drawn from the same distribution or domain, which does not hold in real industrial settings. To mitigate this domain discrepancy issue, prior adversarial domain adaptation methods focused on deriving domain-invariant features. Nevertheless, they overlook target-specific information and inconsistency characteristics pertinent to the degradation stages, resulting in suboptimal performance. To tackle these issues, we propose a novel domain adaptation approach for cross-domain RUL prediction named TACDA. Specifically, we propose a target domain reconstruction strategy within the adversarial adaptation process, thereby retaining target-specific information while learning domain-invariant features. Furthermore, we develop a novel clustering and pairing strategy for consistent alignment between similar degradation stages. Through extensive experiments, our results demonstrate the remarkable performance of our proposed TACDA method, surpassing state-of-the-art approaches with regard to two different evaluation metrics. Our code is available at https://github.com/keyplay/TACDA.
