College of Mechanical Engineering, Xi'an University of Science and Technology
Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control

综采设备逐渐趋于大型化、复杂化、智能化，定期维修与事后维修等传统设备管理模式已难以满足煤矿智能化建设对设备运行的高可靠性需求。因此，研究综采设备健康状态评估相关理论及技术对煤矿智能开采技术发展意义重大。给出了综采设备健康状态评估范畴界定及综采设备健康状态评估流程。从综采设备信号获取、特征提取及融合、健康状态等级划分、健康状态评估模型建立4个方面总结了综采设备健康状态评估方法的研究现状和发展动态。分别从综采设备信号获取及传感器优化布置、数据处理及特征提取、健康状态评估模型建立、综采设备状态评估应用等方面分析了综采设备健康状态评估相关技术目前面临的挑战。针对上述研究现状及面临的挑战，从数据采集方案及故障机理研究手段提升、大数据高性能计算平台建设、深度学习评估模型建立、综采设备健康状态动态评估模型研究、综采设备健康状态评估系统开发等方面探讨了综采设备健康状态评估技术的发展趋势，指出在煤矿智能化进程中，需确保综采设备健康状态评估理论研究、算法开发和工程应用三线齐头并进。

Fully mechanized mining equipment is gradually becoming larger, more complex and more intelligent. The traditional equipment management methods of regular maintenance and post maintenance are no longer able to meet the high reliability requirements of equipment operation in coal mine intelligent construction. Therefore, studying the relevant theories and technologies of fully mechanized equipment health status assessment has great practical significance for coal mine intelligent mining. This paper proposes the scope definition of fully mechanized mining equipment health status assessment and the fully mechanized mining equipment health status assessment process. This paper summarizes the research status and development trends of comprehensive mining equipment health status assessment methods from four aspects: signal acquisition, feature extraction and fusion, health status level classification, and health status assessment model establishment. The current challenges faced by fully mechanized mining equipment health status assessment related technologies are analyzed from aspects such as signal acquisition and sensor optimization layout, data processing and feature extraction, establishment of health status assessment models, and application of fully mechanized mining equipment status assessment. In response to the current research status and challenges mentioned above, the development trend of fully mechanized mining equipment health status assessment technology is discussed from the aspects of improving data collection schemes and fault mechanism research methods, building high-performance big data computing platforms, establishing deep learning assessment models, researching dynamic evaluation models for fully mechanized mining equipment health status, and developing fully mechanized mining equipment health status assessment systems. It is pointed out that in the process of coal mine intelligence, it is necessary to ensure that the theoretical research, algorithm development, and engineering application of fully mechanized mining equipment health status assessment go hand in hand.

国家自然科学基金项目（51834006, 52275131）；中国博士后科学基金项目（2022MD713793）。

曹现刚，段雍，赵江滨，等. 综采设备健康状态评估研究综述[J]. 工矿自动化，2023，49（9）：23-35, 97.

CAO Xiangang, DUAN Yong, ZHAO Jiangbin, et al. Summary of research on health status assessment of fully mechanized mining equipment[J]. Journal of Mine Automation，2023，49（9）：23-35, 97.