煤矿设备全寿命周期健康管理与智能维护研究综述  

Research review on life-cycle health management and intelligent maintenance of coal mining equipment

作　　者：曹现刚[1,2] 段雍 王国法 赵江滨 任怀伟 赵福媛[1,2] 杨鑫 张鑫媛 樊红卫 薛旭升[1,2] 李曼 CAO Xiangang;DUAN Yong;WANG Guofa;ZHAO Jiangbin;REN Huaiwei;ZHAO Fuyuan;YANG Xin;ZHANG Xinyuan;FAN Hongwei;XUE Xusheng;LI Man(School of Mechanical Engineering,Xi’an University of Science and Technology,Xi’an 710054,China;Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control,Xi’an 710054,China;China Coal Technology&Engineering Group Co.,Ltd.,Beijing 100013,China)

机构地区：[1]西安科技大学机械工程学院,陕西西安710054 [2]陕西省矿山机电装备智能检测与控制重点实验室,西安710054 [3]中国煤炭科工集团有限公司,北京100013

出　　处：《煤炭学报》2025年第1期694-714,共21页Journal of China Coal Society

基　　金：国家自然科学基金重点资助项目(51834006);国家自然科学基金面上资助项目(52274158);中国博士后科学基金资助项目(2022MD713793)。

摘　　要：近年来,随着煤矿智能化技术快速发展,煤矿设备全寿命周期健康管理与智能维护技术作为实现煤矿设备运行健康状态智能感知、智能识别和维护决策,保障煤矿设备高效可靠运行的重要手段,相关研究受到了广泛关注。然而,目前煤矿仍然以事后维修、预防维修等方式为主,难以满足煤矿设备的高可靠性需求。基于此,综述了煤矿设备全寿命周期健康管理与智能维护的研究进展以推动其在煤矿的应用,阐释了煤矿设备全寿命周期的健康管理与智能维护内涵,给出了煤矿设备健康管理与智能维护总框架。从煤矿设备大数据管理方法、健康状态评估方法、剩余使用寿命预测方法、智能维护决策方法4个方面分析了煤矿设备健康管理与智能维护方法研究现状。在煤矿设备大数据管理方面,总结了煤矿设备多源信息感知、大数据清洗、大数据集成及存储方法的最新研究成果,深入分析对比了相关方法的应用情况,指出了现阶段煤矿设备大数据管理存在的挑战。在煤矿设备健康状态评估方面,从煤矿设备监测信号特征提取、健康状态等级划分、健康状态评估模型构建3个方面出发探讨了煤矿设备健康状态评估关键方法最新发展现状,对比分析了不同方法的优缺点,总结了该领域面临的难题。在煤矿设备剩余使用寿命预测方面,分析了统计模型方法、物理模型方法和数据驱动方法在煤矿设备剩余使用寿命预测上的优缺点,指出了煤矿设备剩余使用寿命方法存在的问题。在煤矿设备智能维护决策方面,明确了煤矿设备预测性维护决策主要步骤,对比分析了煤矿设备智能维护方法最新研究成果及其优缺点,归纳了现阶段煤矿设备智能维护方法研究的不足。结合煤矿设备全寿命周期健康管理与智能维护面临的挑战及发展要求,从煤矿设备大数据管理方法、时变工况下设备健康In recent years,with the rapid development of intelligent technology in coal mines,the whole life cycle health management and intelligent maintenance technology of coal mine equipment has attracted wide attention.It is an essential means to realize intelligent perception,intelligent identification,and maintenance decisions of the health status of coal mine equipment and ensure its efficient and reliable operation.However,at present,the coal mine is still primarily based on post-maintenance and preventive maintenance,which is challenging to meet the high-reliability requirements of coal mine equipment.Based on this,this paper reviews the research progress of the whole life cycle health management and intelligent maintenance of coal mine equipment to promote its application in coal mines.The connotation of health management and intelligent maintenance for coal mine equipment is explained,and the general framework of health management and intelligent maintenance for coal mine equipment is given.The research analyzes the status of coal mine equipment health management and intelligent maintenance technology from four perspectives:big data management,health status assessment,remaining useful life prediction,and intelligent maintenance decision-making technology.In the big data management of coal mine equipment,the latest achievements of multi-source information perception,big data cleaning,and big data integration and storage of coal mine equipment are summarized,the application of the relevant method is analyzed and compared,and the existing challenges of these methods are pointed out.In terms of coal mine equipment health status assessment,the latest development statuses of key methods are discussed from three aspects of feature extraction,health status classification,and health status assessment model construction,then the advantages and disadvantages of different methods are compared and analyzed,and the problems faced in this field are summarized.In the remaining useful life prediction of coal mine equipment,the a

关 键 词：煤矿设备 大数据管理 健康状态评估 剩余使用寿命预测 智能维护决策 

分 类 号：TD407[矿业工程—矿山机电]