A Large Language Model‐Based Approach for Fault Detection and Its Application
In industrial processes, the infrequent and unpredictable faults often lead to small sample sizes and a limited amount of labeled fault data. Traditional data‐driven methods struggle under these conditions, as they typically rely on large volumes of labeled fault data for effective training. To overcome these limitations, an exploratory fault detection method based on the pre‐trained large language model (LLM) is proposed. The pre‐trained LLM is utilized to extract fault features from small, imbalanced datasets without the explicit labels. Additionally, a stepwise tuple‐based validation process is introduced to ensure logical consistency in fault analysis and to mitigate the effects of potential LLM hallucinations. The proposed method is validated using a case study involving a sudden feedstock (coal) shortage fault in a circulating fluidized bed boiler. The results demonstrate an improvement in fault detection accuracy over conventional methods in this case study (achieving an accuracy of 0.948 and an F1‐score of 0.926), confirming the feasibility of the proposed approach. The viability of integrating LLM‐assisted fault detection into specific industrial scenarios is investigated in this work.