Shape monitoring of scraper conveyor based on inertial measurement unit
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摘要: 刮板输送机作为综采工作面的核心运输装备，准确感知其形态是提升其带载能力、缓解传动冲击、改善综采工作面直线度的重要前提。目前常用的刮板输送机形态间接测量方法难以准确表征其形态，导致测量模型误差较大。针对该问题，采用惯性测量单元直接测量刮板输送机中部槽原始位姿信息，实现刮板输送机形态数据的准确获取。采用融合Heursure阈值规则和新阈值函数的小波阈值去噪方法滤除中部槽运动加速度信号中的噪声干扰，在此基础上分析了中部槽运动特征，设计了基于随机森林的中部槽运动状态识别模型，根据运动状态识别结果采用不同的策略更新中部槽位置，减小了随时间累计的IMU数据误差，提升了IMU位置解算精度。设计了改进哈里斯鹰优化（HHO）算法优化无迹卡尔曼滤波（UKF）进行中部槽姿态解算，通过实验验证了该方法解算的姿态角满足中部槽姿态测量要求。搭建了刮板输送机形态监测实验平台，对基于运动状态识别和改进HHO优化UKF的刮板输送机形态解算方法进行实验验证，结果表明：刮板输送机进行单次推溜且步距为250 mm时，由10节中部槽组成的刮板输送机在底板水平工况下，X，Y轴方向上位移的最大累计误差分别为6.4，8.4 mm，Z轴方向上位移始终保持不变，俯仰角、横滚角和航向角的最大累计误差分别为−0.148，−0.035，0.457º；在底板起伏工况下，X，Y，Z轴方向上位移的最大累计误差分别为6.6，11.5，6.9 mm，俯仰角、横滚角和航向角的最大累计误差分别为−0.540，−0.157，0.817º。该方法可有效抑制累计误差，降低测量误差，实现刮板输送机形态的准确感知。Abstract: Scraper conveyor is the core transportation equipment of the fully mechanized working face. Accurately perceiving its form is an important prerequisite to enhance its carrying capacity, alleviate the transmission impact, and improve the straightness of fully mechanized working face. The commonly used indirect measurement methods for the shape of scraper conveyors are difficult to accurately characterize their shape, resulting in significant measurement model errors. To address this issue, an inertial measurement unit is used to directly measure the original pose information of the middle trough of scraper conveyor, achieving accurate acquisition of the shape data of scraper conveyor. A wavelet thresholding denoising method that combines Heursure threshold rules and a new threshold function is used to filter out noise interference in the acceleration signal of the middle trough. Based on this, the motion features of the middle trough are analyzed, and a middle trough motion state recognition model based on random forest algorithm is designed. Based on the motion state recognition results, different strategies are used to update the position of the middle trough. It reduces the accumulated IMU data error over time and improves the precision of IMU position calculation. The improved Harris hawk optimization (HHO) algorithm unscented Kalman filter (UKF) is designed for middle trough attitude calculation. It is verified through experiments that the attitude angle calculated by this method meets the requirements of middle trough attitude measurement. The experimental platform for shape monitoring of scraper conveyors is constructed. It conducts experimental verification on the shape calculation method of scraper conveyors based on motion state recognition and improved HHO optimized UKF. The results show that when the scraper conveyor performs a single sliding with a step distance of 250 mm, the maximum cumulative errors of displacement in the X and Y directions of the scraper conveyor composed of 10 middle troughs are 6.4 mm and 8.4 mm respectively under the horizontal working condition of bottom plate. It remains unchanged in the Z direction. The maximum cumulative errors of pitch angle, roll angle, and heading angle are −0.148°, −0.035°, and 0.457° respectively. Under the working condition of floor undulation, the maximum cumulative errors of displacement in the X, Y, and Z directions are 6.6 mm, 11.5 mm, and 6.9 mm respectively. The maximum cumulative errors of pitch angle, roll angle, and heading angle are −0.540°, −0.157°, and 0.817° respectively. This method can effectively suppress cumulative errors, reduce measurement errors, and achieve accurate perception of the shape of the scraper conveyor.
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图 1 中部槽位姿测量坐标系
Figure 1. Coordinate system for position and attitude measurement of middle trough
图 2 刮板输送机形态测量坐标系
Figure 2. Coordinate system for shape measurement of scraper conveyor
图 5 改进前后的收敛因子和逃逸能量对比
Figure 5. Comparison of convergence factor and escape energy before and after improvement
图 6 基于改进HHO优化UKF的中部槽姿态解算流程
Figure 6. Flow of attitude calculation of middle trough based on UKF optimized by improved HHO
图 7 中部槽姿态解算实验装置
Figure 7. Experimental equipment of attitude calculation of middle trough
图 8 中部槽模型姿态解算实验结果
Figure 8. Experimental results of attitude calculation of middle trough model
图 9 刮板输送机形态监测实验方案
Figure 9. Experimental scheme for shape monitoring of scrapper conveyor
图 10 刮板输送机形态监测实验平台
Figure 10. Experimental platform for shape monitoring of scrapper conveyor
图 11 底板水平工况下刮板输送机形态监测实验
Figure 11. Shape monitoring experiment of scrapper conveyor under the condition of horizontal floor
图 12 底板水平工况下中部槽测量点位置
Figure 12. Measuring point position of middle trough under the condition of horizontal floor
图 13 底板水平工况下测量点位置在XOY平面和XOZ平面的投影及误差
Figure 13. Projection and error of measuring point position on XOY and XOZ plane under the condition of horizontal floor
图 14 底板水平工况下中部槽初始姿态角
Figure 14. The initial attitude angle of middle trough under the condition of horizontal floor
图 15 底板水平工况下中部槽终止姿态角
Figure 15. The final attitude angle of middle trough under the condition of horizontal floor
图 16 底板起伏工况下刮板输送机形态监测实验
Figure 16. Shape monitoring experiment of scrapper conveyor under the condition of undulating floor
图 17 底板起伏工况下中部槽测量点位置
Figure 17. Measuring points position of middle trough under the condition of undulating floor
图 18 底板起伏工况下测量点位置XOY平面和XOZ平面投影
Figure 18. Projection of measuring point position on XOY and XOZ plane under the condition of undulating floor
图 19 测量点三轴方向的位置累计误差
Figure 19. Accumulated position error of measuring point in the three-axis direction
图 20 底板起伏工况下中部槽初始姿态角
Figure 20. The initial attitude angles of middle trough under the condition of undulating floor
图 21 底板起伏工况下中部槽终止姿态角
Figure 21. The final attitude angle of middle trough under the condition of undulating floor
表 1 样本特征数据
Table 1. Feature data of samples
序号 F3 F4 F7 F8 1 0.696 4 0.652 9 0.046 0 0.409 7 2 0.684 0 0.650 6 0.054 8 0.390 5 ︙ ︙ ︙ ︙ ︙ 1 001 0.826 7 −0.248 5 0.342 7 0.770 5 ︙ ︙ ︙ ︙ ︙ 2 000 0.474 0 −0.678 3 0.137 9 0.287 7 
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表 2 中部槽运动状态识别结果
Table 2. Recognition results of motion states of middle trough
序号 状态 准确率/% 1 S1 100 2 S2 96.4 3 S3 100 4 S4 97.9
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表 3 4种姿态解算算法的误差比较
Table 3. Error comparison of four attitude calculation algorithms
(°) 指标 EKF UKF HHO优化UKF 改进HHO优化UKF 航向角 最大误差 0.466 0.437 0.404 0.193 平均绝对值误差 0.251 0.198 0.165 0.057 横滚角 最大误差 −0.008 −0.005 −0.003 −0.003 平均绝对值误差 0.002 5.499×10−4 6.528×10−4 5.188×10−4 俯仰角 最大误差 0.025 0.012 0.011 0.010 平均绝对值误差 0.103 0.005 0.001 5.805×10−4
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