A Construction Method for a Coal Mining Equipment Maintenance Large Language Model Based on Multi-Dimensional Prompt Learning and Improved LoRA

Xiangang Cao, Xulong Wang (), Luyang Shi, Xin Yang, Xinyuan Zhang and Yong Duan
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Xiangang Cao: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Xulong Wang: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Luyang Shi: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Xin Yang: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Xinyuan Zhang: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Yong Duan: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

Mathematics, 2025, vol. 13, issue 10, 1-22

Abstract: The intelligent maintenance of coal mining equipment is crucial for ensuring safe production in coal mines. Despite the rapid development of large language models (LLMs) injecting new momentum into the intelligent transformation and upgrading of coal mining, their application in coal mining equipment maintenance still faces challenges due to the diversity and technical complexity of the equipment. To address the scarcity of domain knowledge and poor model adaptability in multi-task scenarios within the coal mining equipment maintenance field, a method for constructing a large language model based on multi-dimensional prompt learning and improved LoRA (MPL-LoRA) is proposed. This method leverages multi-dimensional prompt learning to guide LLMs in generating high-quality multi-task datasets for coal mining equipment maintenance, ensuring dataset quality while improving construction efficiency. Additionally, a fine-tuning approach based on the joint optimization of a mixture of experts (MoE) and low-rank adaptation (LoRA) is introduced, which employs multiple expert networks and task-driven gating functions to achieve the precise modeling of different maintenance tasks. Experimental results demonstrate that the self-constructed dataset achieves fluency and professionalism comparable to manually annotated data. Compared to the base LLM, the proposed method shows significant performance improvements across all maintenance tasks, offering a novel solution for intelligent coal mining maintenance.

Keywords: MPL-LoRA; coal mining equipment; multi-task maintenance; LLMs (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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