# Research on Large Language Model for Coal Mine Equipment Maintenance Based on Multi-Source Text
## 4 Results and Discussion
### Experimental Results Analysis
#### 4.2.3 Coal Mine Equipment Maintenance Decision  
The maintenance decision analysis capability of the LLM was evaluated on the maintenance decision analysis dataset. Electrical faults, hydraulic faults, mechanical faults, and auxiliary system faults were selected for evaluation. With each fault record as the input and the generated text as the output, the accuracy of the models was calculated by comparing the generated text with the diagnostic labels in the fault records. Looking at Table 5, it is an example of a maintenance decision analysis response. This indicates that the knowledge graph enhancement guides the larger model to incorporate the underlying relationships between the fault phenomena, fault causes, and maintenance decisions in the reasoning process, which improves the model's logical capability and interpretability. Providing DPO methods to correct the model makes the model more suitable to the field of coal mine equipment maintenance, generates more relevant responses, and the model has a strong ability to understand and explain different fault groups.  
The experimental results for each failure group are displayed in Figure 8, where it can be seen that XCoalChat outperformed the other language models in all failure groups. XCoalChat had an average accuracy of 81.77% in all fault groups compared with ChatGLM-6B, LLama 7B, and Bloom-7B, which improved the accuracy by 47.96%, 37.25% and 39.69%, respectively. XCoalChat performed particularly strongly in terms of the hydraulic and mechanical failures, achieving an accuracy of 90.12% and 86.04%, respectively. This demonstrates the robustness of the model to understand and account for different groups of faults and will provide maintenance personnel with more accurate maintenance decision support. Maintenance specialists can refer to XCoalChat for a reference maintenance program, reducing the time spent.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Fault Phenomenon** & **Maintenance Decision** \\ \hline \hline \multicolumn{3}{c}{According to the content of “Mining Equipment Maintenance and Fault Diagnosis”:} \\ \multicolumn{3}{c}{During the operation of the shearer, there are impurities in the main circuit of the hydraulic system for a long time, which will continuously circulate in the main pump, motor, and each valve in the system as well as the leakage of oil in the system, so that the system oil temperature rises, resulting in the aging of sealing components and an increase in the wear of hydraulic components, so the main pump and motor wear is serious, the leakage is increased, the pressure is naturally reduced, and normal work cannot be carried out.} \\ \multicolumn{3}{c}{Maintenance treatment method:} \\ \multicolumn{3}{c}{back pressure valve is low.} \\ \multicolumn{3}{c}{are thoroughly cleaned. If the component is badly damaged, it is best to replace the hydraulic transmission pump case.} \\ \multicolumn{3}{c}{(2) The oil in the main circuit and the fuel tank should be replaced according to the standard, and the back pressure valve should be cleaned.} \\ \multicolumn{3}{c}{(3) Replace the damaged seal and the damaged main pump and motor; the low back pressure is mainly caused by pipeline and oil pollution; and the oil should be updated and cleaned.} \\ \end{tabular}
\end{table}
Table 5: Example of a coal mine equipment maintenance decision.