Research on fault prediction and empirical study of intelligent underground coal mine equipment based on two-stage Grey–Markov method

Equipment utilized in intelligent underground coal mines necessitates clear distinction from general equipment due to its unique exposure to the specialized subterranean environment. This exposure results in distinct failure patterns, usage, and maintenance protocols. Historically, issues such as incomplete failure data records have impeded accurate failure prediction, leading to a reliance on experience-based maintenance practices for underground mining equipment. To enhance the accuracy of fault prediction for coal mine machinery, this paper proposes a two-stage fault prediction method based on Grey–Markov method. Leveraging daily maintenance data of intelligent dispatching center, this study employs a Grey model to generate initial failure rate forecasts, followed by the integration of the Markov model to refine prediction accuracy. Based on the daily maintenance data from the intelligent dispatching center, the anchor digging integrated machine and conveyor are selected as the case study equipment. The results show that the prediction accuracy of Grey–Markov model is higher than that of the control group.