Equipment operation fault diagnosis method based on the Monte Carlo tree search and the large language modeling

Traditional fault diagnosis methods usually involve complex data processing as well as pattern definition, long diagnosis and analysis times, and poor interpretability, which are not applicable to real-time production control. In this paper, we propose an equipment operation fault diagnosis method based on a Monte Carlo tree search and large language modeling. By modeling the equipment operation fault problem as a search space, representing the possible operation states of the equipment and the different causes of faults as states, and defining the operation behaviors and decision-making behaviors as the transfer of states, and then using the Monte Carlo tree search to simulate the operation logic tree of the equipment in a specific state, the operation of the equipment, the faults detection, and the execution of the operations can be simulated. By using the prior knowledge as well as the reasoning capabilities in the large language model, the operation of the device in the current state is simulated, and the results of the simulated operation including possible faults and other critical information are generated. Finally, the results of the simulation are evaluated by assigning an appropriate score to each state to indicate the value in solving the problem. By combining the production capabilities of the language modeling and outputting the natural language diagnostic explanations and suggestions, the interpretability as well as the real-time nature of the diagnostic method are effectively improved, and the inspection efficiency as well as the detection accuracy are enhanced. The proposed method was employed in the fault diagnosis for large-scale coal mining equipment. Compared with the traditional Monte Carlo tree search method, the fine-tuned Llama2 language model inference method and the Llama2 direct inference method, the accuracy of our new method is improved by 5.9%, 12.2% and 23.3%, and the task success rate is improved by 9.3%, 21% and 28%, respectively.