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基于贝叶斯网络和仿真分析的矿山机电系统可靠性评估

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为增加矿山机电系统的可靠性,及时发现并预防潜在故障,提高矿山生产效率、确保工人安全及设备正常运行,建立了故障信息数据诊断系统,收集矿山机电系统故障数据,同时构建了贝叶斯故障网络,并利用ITE结构将其转化为二元决策图,对机电系统故障进行定性和定量分析.结果显示,在不同挖掘软件上,研究构建模型对机电系统故障的诊断准确率均达 98%以上.在对某矿山机电系统实际评估中,测得绝缘老化或损坏、过载或过热、电子元件故障、内部短路对系统故障影响重要度最大,约为 0.972.该故障分析系统有效提高了机电系统故障诊断的准确性和效率,对底层故障节点进行了有效评估,可为同类型机电系统的故障诊断和可靠性评估提供参考.
Reliability assessment of mine mechanical and electrical systems based on Bayesian networks and simulation analysis
To enhance the reliability of mine mechanical and electrical systems,promptly detect and prevent potential failures,improve mining production efficiency,ensure worker safety,and maintain normal equipment operation,a fault information data diagnostic system was established to collect fault data from mine mechanical and electrical systems.A Bayesian fault network was constructed and converted into a binary decision diagram using the ITE structure for qualitative and quantitative analysis of system faults.The results show that the constructed model achieved a diagnostic accuracy rate of over 98%across different mining software platforms.In an actual evaluation of a mine mechanical and electrical system,insulation aging or damage,overload or overheating,electronic component failures,and internal short circuits were found to have the highest impact on fault,approximately 0.972.This significantly improved the accuracy and efficiency of fault diagnosis in mechanical and electrical systems and provided an effective assessment of underlying fault nodes.The findings can serve as a reference for fault diagnosis and reliability assessment in similar mechanical and electrical systems.

Bayesian networkmine mechanical and electrical systembinary decision diagramfault diagnosissimulation analysisreliability assessment

石宝刚

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陕西国防工业职业技术学院

贝叶斯网络 矿山机电系统 二元决策图 故障诊断 仿真分析 可靠性评估

2023年工业智联网陕西省高校工程研究中心资助项目2024年度机械行业职业教育"产科教协同创新"课题

2023GY40JXHYZX2024102

2024

10.11792/hj20240907

黄金
长春黄金研究院

黄金

CSTPCD
影响因子：0.446
ISSN：1001-1277
年,卷(期)：2024.45(9)