Fault detection in nonstationary industrial processes via kolmogorov-arnold networks with test-time training

In industrial process monitoring, factors such as production schedule changes, equipment aging, and environmental disturbances often lead to shifts in the underlying data distribution. These distributional changes tend to increase false alarm rates and undermine the reliability and adaptability of traditional fault detection methods, thereby compromising the safe and stable operation of industrial facilities. To address these critical challenges, a lifelong fault detection strategy that integrates a Kolmogorov-Arnold Network (KAN) with a novel test-time training (TTT) mechanism is proposed in this paper. Unlike conventional hybrid models, the proposed method introduces a new adaptation framework, in which reconstruction errors dynamically guide selective parameter updates. This allows the model to continuously adapt to distributional shifts without requiring retraining or labeled target data. During online test operation, the model automatically refines its hidden representations using trusted normal samples through a lightweight online update mechanism, thereby improving generalization and robustness under nonstationary conditions. Comprehensive experiments on a widely recognised benchmark dataset from the chemical industry demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches, achieving 92.7 % correct monitoring rate with 0 % false alarm rate under nonstationary conditions.