TARD: Test-time Domain Adaptation for Robust Fault Detection under Evolving Operating Conditions

TL;DR

TARD introduces a test-time domain adaptation method that enhances fault detection robustness in industrial systems operating under evolving conditions, addressing the challenge of limited training data and distribution shifts.

Contribution

The paper presents a novel framework that separates system parameters and sensor data for targeted domain adaptation, improving fault detection in changing environments.

Findings

Significant improvement in fault detection accuracy.

Enhanced robustness of models under real-world variability.

Effective adaptation to evolving operating conditions.

Abstract

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.