基于Spark的煤矿设备密封组件老化故障智能检测模型

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中图分类号：TH17；TQ515 文献标志码：A 文章编号：1001-5922（2026）1-0061-04

Abstract：The safeand eficient operation of coal mine equipment is crucial for ensuring energy supply.In view of theharsh service environment of coal mine equipment，especially the problem that components containing organic materials are prone to aging and failure，an intelligent XGBoost deep learning fault diagnosis model based on the Spark computing framework was constructed.The built model uses Spark to process the massive operation data of coal mineequipment，andatthe same time takes thedataas the training and testing data ofthe XGBoost model for deep learning，soas to realize the inteligent diagnosis of equipment faults.The proposed intelligent XGBoost deep learning fault diagnosis model based onthe Spark computing framework wascomparedwith therandom forestmodel and the Hadoop computing framework.The results show thatthe proposed inteligent equipment fault diagnosis modelhas a high accuracy in identifying equipmentfault types，and itsruning time is lessthan 1/40ofthatof the Hadoop computing framework.This has certain reference value for the intelligent diagnosis of coal mine equipment faults and ensuring the safe and stable operation of equipment.

Key words：sprak computing framework;XGBoost deep learning algorithm;coal mine equipment;fault diagnosis

煤矿作为重要的能源生产基地，其设备运行状 态直接影响到煤矿的生产效率与安全水平。（剩余6164字）