Fault detection is crucial in industrial systems to prevent failures and
optimize performance by distinguishing abnormal from normal operating
conditions. Data-driven methods have been gaining popularity for fault
detection tasks as the amount of condition monitoring data from complex
industrial systems increases. Despite these advances, early fault detection
remains a challenge under real-world scenarios. The high variability of
operating conditions and environments makes it difficult to collect
comprehensive training datasets that can represent all possible operating
conditions, especially in the early stages of system operation. Furthermore,
these variations often evolve over time, potentially leading to entirely new
data distributions in the future that were previously unseen. These challenges
prevent direct knowledge transfer across different units and over time, leading
to the distribution gap between training and testing data and inducing
performance degradation of those methods in real-world scenarios. To overcome
this, our work introduces a novel approach for continuous test-time domain
adaptation. This enables early-stage robust anomaly detection by addressing
domain shifts and limited data representativeness issues. We propose a
Test-time domain Adaptation Anomaly Detection (TAAD) framework that separates
input variables into system parameters and measurements, employing two domain
adaptation modules to independently adapt to each input category. This method
allows for effective adaptation to evolving operating conditions and is
particularly beneficial in systems with scarce data. Our approach, tested on a
real-world pump monitoring dataset, shows significant improvements over
existing domain adaptation methods in fault detection, demonstrating enhanced
accuracy and reliability.

我们的研究工作介绍了一种新颖的连续测试时域自适应异常检测方法，通过解决领域变化和有限数据代表性问题，实现了早期稳健的异常检测，能够有效适应不断变化的操作条件，尤其对于数据稀缺的系统具有显著改进，并在故障检测中表现出增强的准确性和可靠性。