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评估方法、多因素影响下设备剩余使用寿命方法、煤矿设备多目标智能维护决策方法、健康管理与智能维护算法集成及系统开发等方面对煤矿设备健康管理与智能维护提出了展望，指明了煤矿设备健康管理与智能维护关键理论、方法的研究方向，为提升煤矿设备健康管理及智能维护水平，促进煤炭工业转型升级和高质量发展提供依据。

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 advantages and disadvantages of the statistical model method, physical model method, and data-driven method are compared, and the problems of existing methods are expounded. In terms of intelligent maintenance of coal mine equipment, the main steps of coal mine equipment predictive maintenance are defined, the latest research results of intelligent maintenance methods of coal mine equipment and their advantages and disadvantages are compared and analyzed, and the deficiencies of the current research on intelligent maintenance decision technology are summarized. Combined with the challenges and development requirements, the prospect of coal mine equipment health management and intelligent maintenance technology is explored from the aspects of big data management, health status assessment under time-varying working conditions, remaining useful life prediction under the influence of multiple factors, multi-objective intelligent maintenance decision-making, algorithm integration and system development of coal mine equipment. The research direction of critical theories and methods of health management and intelligent maintenance for coal mine equipment is pointed out, which provides a basis for improving the level of health management and intelligent maintenance of the coal mine equipment and promoting the transformation and upgrading of coal industry and high-quality development.