Fault detection is essential in complex industrial systems to prevent failures and optimize performance by distinguishing abnormal from normal operating conditions. With the growing availability of condition monitoring data, data-driven approaches have increasingly applied in detecting system faults. However, these methods typically require large, diverse, and representative training datasets that capture the full range of operating scenarios, an assumption rarely met in practice, particularly in the early stages of deployment. Industrial systems often operate under highly variable and evolving conditions, making it difficult to collect comprehensive training data. This variability results in a distribution shift between training and testing data, as future operating conditions may diverge from those previously observed ones. Such domain shifts hinder the generalization of traditional models, limiting their ability to transfer knowledge across time and system instances, ultimately leading to performance degradation in practical deployments. To address these challenges, we propose a novel method for continuous test-time domain adaptation, designed to support robust early-stage fault detection in the presence of domain shifts and limited representativeness of training data. Our proposed framework --Test-time domain Adaptation for Robust fault Detection (TARD) -- explicitly separates input features into system parameters and sensor measurements. It employs a dedicated domain adaptation module to adapt to each input type using different strategies, enabling more targeted and effective adaptation to evolving operating conditions. We validate our approach on two real-world case studies from multi-phase flow facilities, delivering substantial improvements in both fault detection accuracy and model robustness over existing domain adaptation methods under real-world variability.

翻译：故障检测在复杂工业系统中至关重要，其通过区分异常与正常运行状态来预防故障并优化性能。随着状态监测数据的日益丰富，数据驱动方法在系统故障检测中的应用日渐广泛。然而，这些方法通常需要大规模、多样化且具有代表性的训练数据集，以覆盖全部运行场景，这一假设在实践中很少得到满足，尤其在部署初期。工业系统常在高度动态变化的条件下运行，使得收集全面的训练数据变得困难。这种可变性导致训练数据与测试数据之间存在分布偏移，因为未来的运行条件可能偏离先前观测到的状态。此类域偏移阻碍了传统模型的泛化能力，限制了其跨时间和系统实例的知识迁移，最终导致实际部署中的性能下降。为应对这些挑战，我们提出了一种新颖的持续测试时域自适应方法，旨在支持存在域偏移且训练数据代表性有限情况下的鲁棒早期故障检测。我们提出的框架——面向鲁棒故障检测的测试时域自适应方法（TARD）——明确将输入特征分离为系统参数与传感器测量值。该框架采用专用域自适应模块，通过不同策略对每种输入类型进行自适应，从而实现对动态运行条件更具针对性且更有效的适应。我们在多相流设施的两个实际案例研究中验证了所提方法，结果表明相较于现有域自适应方法，在真实世界可变性条件下，本方法在故障检测精度与模型鲁棒性方面均取得显著提升。