Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions

The paper presents a novel Test-time domain Adaptation Anomaly Detection (TAAD) framework for enhancing early fault detection in industrial systems by effectively adapting to evolving operating conditions in real-time, using limited normal data samples without the need for labeled faulty data.

Abstract

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.

Method

The authors used a reconstruction-based anomaly detection framework that incorporates an adaptive module for test-time domain adaptation. This framework is designed to adapt to domain shifts in real-time by separating input variables into system parameters and measurements, and then employing two domain adaptation modules to independently adapt to each category. The adaptive module is trained on a few target data samples to predict compensation values for the domain gap between source and target data, and it includes an AdaBN layer to update its mean and variance based on batch statistics during test time. The pre-trained autoencoder is then combined with this adaptive module to enable robust anomaly detection across different domains.

Main Finding

The authors discovered that their proposed Test-time domain Adaptation Anomaly Detection (TAAD) framework significantly improved fault detection in industrial systems compared to existing domain adaptation methods. The TAAD framework effectively adapted to evolving operating conditions and was particularly beneficial in systems with scarce data. The authors tested their approach on a real-world pump monitoring dataset, where TAAD showed enhanced accuracy and reliability in fault detection. The framework's ability to distinguish between normal variations and abnormal changes in operating status, and its capability to adapt to continuous domain shifts, were key findings that demonstrated the potential of TAAD for robust anomaly detection in dynamic industrial environments.

Conclusion

The conclusion of the paper is that the authors propose an effective continuous test-time domain adaptation approach, TAAD, for efficient and robust anomaly detection under evolving operating conditions. This approach does not require labeled faulty data and needs only a minimal amount of normal data samples for adaptation, which aligns well with the practical needs of real-world industrial systems. The experimental results demonstrate TAAD's effectiveness in achieving early fault detection under significant domain shifts, both across different stations and over time, while maintaining a low false alarm rate. Despite its satisfying performance, the authors suggest potential improvements, such as re-training the adaptive module upon significant domain shift detection and automatic adjustment of the thresholding parameter for optimized trade-off between minimizing false alarms and prompt fault detection.

Keywords

Fault detection, domain adaptation, test-time adaptation, anomaly detection, industrial systems, evolving operating conditions, continuous domain shifts, reconstruction-based methods, autoencoder, unsupervised learning, fleet approaches, prognostics and health management, pump monitoring dataset, early fault detection, robust anomaly detection, data-driven methods, condition monitoring data, domain shift, adaptation modules, control parameters, sensor measurements, batch normalization, AdaBN layer, thresholding parameter, false alarm rate, adaptation performance, significant domain shifts, adaptation training, empirical determination, automatic adjustment, trade-off optimization, practical needs, real-world industrial systems, experimental results, comparison with other methods, low false alarm rate, dynamic industrial environments, scarce data, normal data samples, adaptation, domain gap, operating condition domain shift, compensation values.