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煤矿掘进机光纤惯导自适应零速修正方法

Adaptive zero velocity correction method for fiber optic inertial navigation system in coal mining roadheader

  • 摘要: 高精度光纤惯导与高精度位置传感器融合定位是实现煤矿巷道掘进机精确定位的有效方法,但高精度光纤惯导成本较高且误差随时间累积,如何通过低成本、低精度的光纤惯导自适应零速修正达到高精度光纤惯导性能和消除累积误差是目前亟待解决的问题。因此,提出一种零速检测与扩展卡尔曼滤波结合的煤矿掘进机光纤惯导自适应零速修正方法。针对掘进机光纤惯导传统阈值方法零速检测不准确问题,提出一种基于PCA−SCSO−SVM(Principal Component A-nalysis, PCA; Sand Cat Swarm Optimization, SCSO; Support Vector Machine, SVM)的零速检测方法,该方法利用掘进机振动信号进行零速检测,首先对振动信号进行VMD分解并根据相关系数选取IMF分量,其次提取IMF分量的时频域特征,并运用主成分分析法降维以降低诊断模型复杂度与数据分析难度,最后通过引入沙猫群优化算法优化核函数与惩罚参数提高零速检测的准确率。针对高精度光纤惯导成本较高和误差随时间累积问题,提出一种自适应零速修正方法,该方法根据掘进机零速检测结果和掘进机运动特性确定的修正间隔时间,利用扩展卡尔曼滤波在零速时刻的速度误差和角速度误差作为观测量进行自适应零速修正。为了验证本文方法的有效性,开展了零速检测和零速修正的试验验证。零速检测试验中,将本文方法、SVM方法、 GA−SVM方法和PSO−SVM方法进行对比,结果表明:本文方法零速检测准确度最高,达到了96.5%。零速修正试验结果表明:研究提出的零速修正方法能有效降低光纤惯导的姿态误差和提升掘进机姿态检测精度,且修正间隔时间越短误差估计越准确、修正后的姿态精度越高,修正间隔时间为10 min时,能够使0.1(°)/h的光纤惯导达到0.057(°)/h的姿态检测精度,实现了低精度光纤惯导达到高精度定位目标。

     

    Abstract: The fusion positioning of high-precision fiber-optic inertial navigation and high-precision position sensor is an effective method to realize the accurate positioning of coal mine roadway roadheader. However, the high-precision fiber-optic inertial navigation has high cost and the error accumulates with time. How to achieve high-precision fiber-optic inertial navigation performance and eliminate cumulative error through low-cost and low-precision fiber-optic inertial navigation adaptive zero-speed correction is an urgent problem to be solved. Therefore, an adaptive zero-speed correction method for fiber-optic inertial navigation of coal mine roadheader based on zero-speed detection and extended Kalman filter is proposed.Aiming at the problem of inaccurate zero-speed detection of traditional threshold method for roadheader fiber-optic inertial navigation, a zero-speed detection method based on PCA−SCSO−SVM ( Principal Component Analysis PCA, Sand Cat Swarm Optimization SCSO, Support Vector Machine SVM ) is proposed. This method uses roadheader vibration signal for zero-speed detection. Firstly, the vibration signal is decomposed by VMD and the IMF component is selected according to the correlation coefficient. Secondly, the time-frequency domain features of IMF components are extracted, and the principal component analysis method is used to reduce the dimension to reduce the complexity of the diagnostic model and the difficulty of data analysis. Finally, the accuracy of zero-speed detection is improved by introducing the sandcat swarm optimization algorithm to optimize the kernel function and penalty parameters.Aiming at the problem of high cost and error accumulation with time of high-precision fiber-optic inertial navigation, an adaptive zero-speed correction method is proposed. According to the correction interval time determined by the zero-speed detection results of the roadheader and the motion characteristics of the roadheader, the speed error and angular velocity error of the extended Kalman filter at the zero-speed moment are used as observations to perform adaptive zero-speed correction. In order to verify the effectiveness of the proposed method, the experimental verification of zero-speed detection and zero-speed correction is carried out. In the zero-speed detection experiment, this method, SVM method, GA−SVM method and PSO−SVM method are compared. The experimental results show that the zero-speed detection accuracy of this method is the highest, reaching 96.5%.The zero-speed correction experimental results show that the zero-speed correction method proposed in this paper can effectively reduce the attitude error of the fiber-optic inertial navigation and improve the attitude detection accuracy of the roadheader. The shorter the correction interval, the more accurate the error estimation and the higher the corrected attitude accuracy. When the correction interval is 10 minutes, the fiber-optic inertial navigation of 0.1(°)/ h can reach the attitude detection accuracy of 0.057(°)/h, and the low-precision fiber-optic inertial navigation can reach the high-precision positioning target.

     

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