VBStMM-LLM: Robust Fault Identification Leveraging Variational Bayesian Student’s T-Mixture Modeling With Lifelong Learning Mechanism

Modern industrial processes often exhibit non-Gaussian, multimodal, and time-varying behaviors due to fluctuating operating conditions, raw material variations, and external disturbances, such as sensor failures and noise. These complexities pose significant challenges for robust fault detection and identification, critical for ensuring process stability and product quality. To tackle these challenges, this article proposes an innovative robust fault identification framework that leverages a variational Bayesian student’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula>-mixture model (VBstMM) with a lifelong learning mechanism (LLM), named VBStMM-LLM. VBStMM-LLM is particularly effective for multimodal, time-varying industrial processes due to two core innovations: first, a robust mixture modeling approach grounded in the Student’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula>-distribution with variational Bayesian inference, which accurately captures complex process dynamics while mitigating the impact of noise and outliers; and second, the innovational integration of LLM that enables adaptive model updates, preserving historical knowledge, and preventing catastrophic forgetting. Extensive experiments on the Tennessee Eastman benchmark process and the ventilation exhaust fans utilized in a continuous casting line at a Chinese steel smelting facility demonstrate the framework’s superior performance. For the three operational modes of the TE process, VBStMM-LLM achieved fault identification accuracy of 94.5%, 93.6%, and 97.7%, respectively. Remarkably, for the fault diagnosis of VEF task, it achieved a perfect rate of 100%. These results highlight VBStMM-LLM’s exceptional robustness, accuracy, and adaptability, underscoring its potential for widespread industrial applications.