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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0. 引言
为提高矿山安全生产保障能力，国家要求全国煤矿及非煤矿山建立和完善监测监控、人员定位、供水施救、压风自救、通信联络及紧急避险等井下安全避险“六大系统”[1]。其中，井下人员定位系统在遏制井下超定员生产、防止人员进入危险区域、及时发现未按时升井人员、加强特种作业人员管理、加强干部带班管理、实现煤矿井下作业人员考勤管理等工作中发挥着重要作用[2-4]。同时，井下车辆精确定位是矿井安全高效运输的重要保障[5]。目前矿井人员和车辆精确定位主要采用超宽带（Ultra Wide Band，UWB）无线通信技术，可实现厘米级高精度定位，并具有抗多径能力强、系统复杂性低等特点[6-11]。在算法上，主要采用飞行时间（Time of Flight，TOF）定位方法，具有定位精度不受信号发送功率、接收灵敏度和信号传输衰减影响，不需定位分站与定位卡时钟同步等优点，但需双天线或双定位分站联合测距和定向，以辨识定位卡位于定位分站左侧或右侧，不便于天线安装维护[12-14]。因此，本文提出了到达相位差（Phase Difference of Arrival，PDOA）与TOF煤矿井下联合定位方法，通过定位分站的2根天线与定位卡之间的TOF平均值计算定位卡距分站的距离，通过PDOA值判断定位卡在分站的哪一侧，可缩短双天线距离，将双天线集成为一体，便于安装维护。
1. 方法原理
1.1 TOF测距方法
目前应用广泛的TOF测距方法属于双向测距技术，主要利用无线电信号在源节点和目的节点之间的飞行时间来测量二者之间的距离[15-16]。TOF测距原理如图1所示。
源节点A向目的节点B发送一个请求数据包，目的节点B收到数据包并对其进行处理，将周转时间TA封装到应答数据包中并发送给源节点A，源节点A计算出从开始发送请求数据包到接收到目的节点B返回的应答数据包的总时间TZ，用总时间TZ减去周转时间TA就是双方数据包在飞行中度过的往返时间，记为TR。假定在每个方向发生的飞行时间TF等于一半的往返时间：
$$ T_{{\rm{F}}}=T_{{\rm{R}}}/2=(T_{{\rm{Z}}}-T_{{\rm{A}}})/2 $$ (1) 则2个节点之间的距离为
$$ D=cT_{{\rm{F}}} $$ (2) 式中c为光速，c=3×108 m/s。
矿用本安型定位分站为双天线设计，分站通过1 m长的馈线与天线相连。安装时，2根天线在分站相反方向形成与巷道壁平行的直线。2根天线独立与通信覆盖范围内的定位卡测距，利用二者距离差的符号判断定位卡在分站的哪一侧，从而实现对定位卡的一维定位，如图2所示。
1.2 PDOA定位算法
PDOA定位算法具有以下优点：角度估计精度高；可减小用于定向的2根天线之间的距离；将定位分站的2根天线一体化，便于安装维护；定位精度高。基于PDOA的到达角度（Angle of Arrival，AOA）估计原理如图3所示。
从定位卡发送的无线电信号到达定位分站的2根天线，信号路径长度的差别p与天线M，N之间的距离d和AOA值θ满足下式。
$$ p=d{\rm{sin}} \; \theta $$ (3) 2根天线接收的信号中的第一路径（First-Path）的PDOA为
$$\alpha =2{\text{π}} p / \lambda $$ (4) 式中λ为波长。
$$\theta=\arcsin \; (\alpha\lambda / 2 {\text{π}} d) $$ (5) 当d＜λ/2时，θ与α在[−π/2，π/2]上有一一对应关系。
1.3 PDOA与TOF联合定位方法
基于UWB的PDOA与TOF联合定位方案：通过2根天线与定位卡之间的TOF平均值估计定位卡距分站的距离；在天线距离小于信号半波长的情况下，对于煤矿巷道一维定位场景，可以基于PDOA值判断定位卡在分站的哪一侧，而不必求出具体的AOA值。
仅采用TOF技术的定位分站安装时2根天线必须分开一定的距离，且使用支架固定，需要将2根天线的距离录入定位软件系统，而天线较易因其他矿井施工活动误碰而改变位置，使系统稳定性受到影响[20-21]。采用PDOA与TOF联合定位方法的定位分站用仅5 cm长的棒状天线取代接有1 m馈线的平面天线，便于天线角度固定，安装简单，维护方便，有利于节省人力成本，提升系统稳定性。棒状天线和平面天线对比如图4所示。
2. 应用测试
2.1 测试设计
在神东煤炭集团大柳塔煤矿东辅助运输大巷进行测试。巷道宽6 m、高5 m，断面为半圆拱形，巷道平坦、无煤尘。巷道顶部及左右侧巷帮中部有金属管道，整个巷道为水泥墙面，地面有有轨电车轨道，如图5所示。主要测试器材为矿用本安型定位分站、车辆定位卡及相应的固定支撑器材，激光测距仪等。定位分站和车辆定位卡发射的电磁波信号中心频率为4.0 GHz。
本文测试分为PDOA方向测试和TOF精度测试2个部分，布置如图6所示。测试步骤：① 将定位分站与车辆定位卡分别固定到支撑器材上。② 用RS485通信线缆将定位分站与业务化运行的矿井定位系统定位分站连接，开启分站。③ 将定位分站天线固定到巷帮，高度为2 m。④ 调节车辆定位卡到合适位置固定，高度为1.5 m。⑤ 进行PDOA方向测试，车辆定位卡在定位分站两侧位置取样。⑥ 用激光测距仪测量车辆定位卡天线与定位分站天线的距离。⑦ 车辆定位卡与定位分站每1 s通信1次，距离固定后，每个采样点车辆定位卡固定10 s以获得稳定读数。⑧ 移动车辆定位卡远离定位分站，重复测试步骤⑤和步骤⑥。⑨ 进行TOF精度测试，车辆定位卡在定位分站单侧位置取样。⑩ 用激光测距仪测量车辆定位卡天线与定位分站天线的距离。⑪ 距离固定后，每个采样点车辆定位卡固定1 min以获得足够数据。⑫ 移动车辆定位卡远离定位分站，重复测试步骤⑩和步骤⑪。⑬ 测试数据通过定位系统上传至地面服务器，测试完毕后从服务器下载数据到本地。
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图 6 PDOA方向测试和TOF精度测试布置Figure 6. Layout of PDOA directional test and TOF precision test2.2 PDOA方向测试结果
PDOA方向测试结果如图7所示（图中包含41个点位的测试数据，零点是定位分站所在位置），可看出在定位分站两侧，PDOA值符号相反。因此，可以通过PDOA值的符号判断定位卡方向。
2.3 TOF精度测试结果
在距定位分站一侧200 m范围内的18个点位进行TOF精度测试，距离近时测试点间距小，距离远时测试点间距大。将测试点位测试数据的平均值用于误差计算，结果见表1。
表 1 TOF精度测试数据Table 1. TOF precision test data真实距离/m 测量均值/m 绝对误差/m 相对误差/% 1.938 1.895 0.043 2.22 2.922 2.855 0.067 2.29 4.779 4.829 0.050 1.05 8.186 8.206 0.020 0.24 10.984 10.874 0.110 1.00 15.338 15.198 0.140 0.91 20.726 20.616 0.110 0.53 25.446 25.541 0.095 0.37 30.921 30.952 0.031 0.10 40.767 40.696 0.071 0.17 61.731 61.627 0.104 0.17 82.217 82.229 0.012 0.01 100.479 100.398 0.081 0.08 122.302 122.376 0.074 0.06 139.672 139.557 0.115 0.08 159.092 158.951 0.141 0.09 177.285 177.398 0.113 0.06 198.489 198.350 0.139 0.07 将测试点位误差与真实距离绘制成曲线，如图8所示。可看出在约82 m处误差最小，为1.2 cm；在约159 m处误差最大，为14.1 cm，接近定位系统设备所采用的DW1000型UWB定位芯片的设计理论精度10 cm；在测试距离范围内，精度与距离没有明显的相关变化趋势。
由于业务化运行的矿井定位系统有实时展示需求，为保证展示效果，运动中的定位目标轨迹应尽量平滑。所以，系统的定位稳定性较为关键，即在不考虑定位精度的情况下，对同一位置的定位卡进行多次测量，其测量值的分布范围应尽量小。取距定位分站距离最远的198 m处测试点位的测试数据进行分析，在此处共有62个测量值。计算测量值与其均值的离差，将离差分为10组，通过直方图统计各组测量值的频数，结果如图9所示。可看出离差全部在10 cm内，分布近似正态分布。将离差进行正态分布拟合，得到离差分布的标准差为0.033 4。
离差经验分布与正态分布拟合结果的累计概率曲线如图10所示，可看出二者吻合度非常好，表明本文提出的联合定位方法具有良好的稳定性。
3. 结语
采用PDOA与TOF煤矿井下联合定位方法的定位分站用长度仅5 cm的棒状天线取代接有1 m馈线的平面天线，硬件实现方案更简单，维护更方便，有利于节省人力成本，提升系统稳定性。在大柳塔煤矿井下进行了PDOA方向和TOF精度测试，结果表明：利用PDOA值的符号可以正确判断定位卡在定位分站的哪一侧；定位精度在15 cm以内，可为煤矿安全生产提供精准的位置服务；在200 m测试距离范围内，定位精度不受距离远近影响；TOF测距数值稳定在相对其均值±10 cm的范围内，具有良好的稳定性。
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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
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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
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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
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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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