Coal and rock identification method based on Kalman optimal estimation of load data of rocker arm pin axle of shearer
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摘要: 采煤机的煤岩识别技术是实现智能控制的基础，现有煤岩识别方法对现场环境及检测设备要求较高，实际综采工作面难以满足所需的必要条件。针对上述问题，提出了一种基于采煤机摇臂销轴载荷数据卡尔曼最优估计的煤岩识别方法，在不增加外部附属仪器设备的基础上，采用摇臂销轴传感器替换现有销轴感知煤岩载荷，可较好地适应环境。通过测定位于摇臂与连接架连接处摇臂销轴传感器的应变数据，采用卡尔曼最优估计算法对载荷数据进行降噪处理，使采煤机在截割煤岩等不同工况下的载荷区间相互分开，通过判断实时载荷处于的区间实现煤岩识别。构建随机载荷信号，利用卡尔曼最优估计算法、最小均方(LMS)自适应估计算法、变步长LMS自适应估计算法对相同信号进行降噪处理，结果验证了卡尔曼最优估计算法对载荷信号降噪处理的可行性与优越性。在某综采实验平台上进行煤岩识别验证，以空载、截割煤壁、截割岩石3个阶段对煤壁侧上端摇臂销轴沿采煤机牵引方向的载荷进行分析，结果表明：载荷数据未经卡尔曼最优估计算法处理之前，截割煤壁与截割岩石状态下的载荷区间存在重合部分，无法准确完成煤岩识别；载荷数据经过卡尔曼最优估计算法处理后，空载、截割煤壁、截割岩石3种工况下的载荷区间相互分开，且各工况下的载荷区间长度缩短了65.6%~83.3%，均方差降低了66.5%~72.9%，数据波动更小，有效提高了数据的辨识度。在实际工程应用中可根据该方法设定截割煤层状态时的期望载荷受力范围，一旦超出该范围，则判断此时不是截割煤壁状态，从而实现煤岩识别。Abstract: The shearer's coal and rock identification technology is the basis of intelligent control. The existing coal and rock identification method for the site environment and testing equipment requirements are higher. The actual fully mechanized working face is difficult to meet the necessary conditions. In order to solve the above problems, a coal and rock identification method based on the Kalman optimal estimation of the shearer rocker pin axle load data is proposed. On the basis of not increasing external auxiliary instruments and equipment, the rocker pin axle sensor is used to replace the existing pin axle to sense the coal and rock load, which can better adapt to the environment. By measuring the strain data of the rocker pin axle sensor located at the connection between the rocker arm and the connecting frame, the Kalman optimal estimation method is used to reduce the noise of the load data. The load intervals of the shearer under different working conditions such as cutting coal and rock are separated from each other. By judging the interval of the real-time load value, the coal and rock interface can be identified. The identification of coal and rock is verified on a fully mechanized experimental platform. The load of the rocker pin axle at the upper end of the coal wall side along the shearer traction direction is analyzed in three stages: no-load, cutting the coal wall and cutting the rock. The results show that before the load data is processed, there is overlap between the load intervals of cutting the coal wall and cutting the rock, and the coal and rock interface identification cannot be completed accurately. After the load data is processed by the Kalman optimal estimation algorithm, the load intervals under no-load, cutting the coal wall and cutting the rock conditions are separated from each other. In addition, the load interval length of each working condition is shortened by 65.6%-83.3%, and the mean square deviation is reduced by 66.5%-72.9%. The data fluctuation is smaller, which effectively improves data identification. In practical engineering applications, the expected load stress range in the coal seam cutting state can be set according to the method. Once this range is exceeded, it is judged that this is not a cutting the coal wall state and thus coal and rock identification is achieved.
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图 5 多种降噪算法均方误差对比
Figure 5. Comparison of mean square error of various noise reduction algorithms
图 10 空载、截割煤壁和截割岩石工况下摇臂销轴载荷曲线
Figure 10. Load curve of rocker arm pin axle under no-load, cutting the coal wall and cutting the rock
图 11 空载、截割煤壁和截割岩石工况下摇臂销轴载荷最优估计
Figure 11. Optimal estimation of rocker arm pin axle under no-load, cutting the coal wall and cutting the rock
表 1 空载、截割煤壁和截割岩石工况下摇臂销轴载荷及均方差
Table 1. Load and mean variance of rocker arm pin axle under no-load, cutting the coal wall and cutting the rock
采煤机运行状态 载荷/kN 均方差/kN 空载 32.62 ~ 42.06 0.290 3 截割煤壁 −75.99 ~ −13.47 8.366 0 截割岩石 −120.07 ~ −35.93 11.033 0 
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表 2 最优估计处理后空载、截割煤壁和截割岩石工况下摇臂销轴载荷区间及均方差
Table 2. Load interval and mean square difference in each state after optimal estimation
采煤机运行状态 载荷区间/kN 均方差/kN 空载 35.81 ~ 37.39 0.0787 截割煤壁 −50.68 ~ −29.16 2.690 0 截割岩石 −75.33 ~ −58.43 3.697 0
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