Summary of research on health status assessment of fully mechanized mining equipment
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摘要: 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。Abstract: 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.
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图 1 综采设备故障诊断与健康状态比较
Figure 1. Comparison of fault diagnosis and health status of fully mechanized mining equipment
图 2 综采设备故障诊断与健康状态评估的逻辑关系
Figure 2. Logical relationship between fault diagnosis and health status assessment of fully mechanized mining equipment
图 3 综采设备健康状态评估流程
Figure 3. Health status assessment process of fully mechanized mining equipment
图 4 综采装备故障监测与智能诊断系统界面
Figure 4. Interface of fault monitoring and intelligent diagnosis system for fully mechanized mining equipment
表 1 综采关键设备及主要监测信号
Table 1. Key equipment of fully mechanized mining and main monitoring signals
对象 监测信号 采煤机 截割部电动机温度[19-20]、电流[19-20]、转速[20]、振动[19]；牵引部电动机电流[19-20]、温度[19-20]、牵引速度[19-20]；工作面位置及倾角[20]、采高[20]、机身姿态[20]等 刮板输送机 电动机电流[21-22]、电压[21-22]、温度[21-22]；齿轮箱振动[21-22]、油液温度[21-22]、润滑油油位[21-22]等 液压支架 油液压力[23]、支架姿态监测[24]、护板打开采高[25]等 乳化液泵 电动机电流[26-27]、乳化液温度[26-27]、液位[26-27]、压力[26-27]等 转载机 电动机温度、转速、振动、运输速度、胶带裂纹、胶带跑偏等[28] 
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表 2 非线性特征提取主要方法
Table 2. Main methods of nonlinear feature extraction
方法种类 特点 核主成分分析 考虑特征非线性相似性；忽略样本在高维特征空间的局部流形结构 拉普拉斯特征映射 从局部的角度去构建数据之间的关系，没有精确的投影矩阵，映射后能保持原有的数据结构 局部保持投影 有明确的投影矩阵；没有充分利用类标签信息，无法处理高度非线性的数据 等距特征映射 没有精确的投影矩阵 受限玻尔兹曼机 能同时提取数据的初级自然特征和高层次目标特征；需要大量样本 深度置信网络 能通过隐藏层提取数据高层次目标特征；需要大量样本；模型可解释性差
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表 3 综采设备健康状态评估方法对比
Table 3. Comparison of health status assessment of fully mechanized equipment methods
方法种类 优点 缺点 模型驱动 时空复杂度较低，物理意义清晰 数学解析模型的完整性和准确性要求较高 知识驱动 对整机或子系统的横、纵向退化过程有良好解释，时空复杂度低，物理意义清晰 残缺、片面、模糊的先验知识导致模型精度降低，静态模型难以表征设备动态退化过程 数据驱动 需大量状态监测数据，无需专家知识，准确度较高 模型缺乏清晰的物理解释，易受噪声和异常样本干扰
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