基于Apriori算法的煤矿设备控制失败风险挖掘及定位分析

    Risk mining and location analysis of coal mine equipment control failures based on the Apriori Algorithm

    • 摘要: 煤矿井下作业环境复杂,设备多样,人员管理存在问题,导致设备控制失败率高,制约安全生产与运营。本研究聚焦于煤矿设备控制失败环节的精准定位,力求找出导致设备控制失败的关键风险因素。本文首先分析了某煤矿井下各系统,以及所对应的设备和对应的场景,然后采集神东某煤矿2021—2024年7 385组设备控制失败数据,经数据清洗等预处理后,运用Apriori算法挖掘关联规则,并设置支持度、置信度、提升度阈值筛选强关联规则。研究发现“详细地点_风机配电点”与“设备名称_生产移变”组合场景风险高,叠加部分因素失败概率极高,特定操作流程或权限设置可能存在隐患。建议优化硬件联动逻辑,规范操作流程,强化人员培训与监控。

       

      Abstract: The underground operation environment of coal mines is complex, with diverse equipment and existing problems in personnel management, resulting in a high failure rate of equipment control, which restricts safe production and operation. This research focuses on the precise positioning of the failure links of coal mine equipment control, striving to identify the key risk factors leading to equipment control failures. This paper firstly analyzes each system underground in a certain coal mine, the corresponding equipment, and the corresponding scenarios. Then, 7 385 groups of equipment control failure data from Shendong Coal Mine from 2021 to 2024 are collected. After preprocessing such as data cleaning, the Apriori algorithm is used to mine association rules, and thresholds for support, confidence, and lift are set to screen out strong association rules. The research finds that the combined scenario of “Detailed Location_Fan Power Distribution Point” and “Equipment Name_Production Transformer” has a high risk. When some factors are superimposed, the failure probability is extremely high, indicating that there may be potential hazards in specific operation processes or authority settings. It is recommended to optimize the hardware linkage logic, standardize the operation process, and strengthen personnel training and supervision.

       

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