基于分数阶偏微分的井下移动设备在线检测数据融合算法

On-Line Detection Data Fusion Algorithm of Underground Mobile Equipment Based on Fractional Order Partial Differential

针对在应用无线传感网络技术实时采集井下移动设备各类检测数据时,因设备作业时位置处于动态变化之中,检测数据受到检测设备性能、设备工作环境和信号干扰等多重因素影响存在着无法预测的检测误差,致使数据管理中心采集到的检测数据无法反映井下移动设备真实的监测信息,直接影响着设备运行安全性和生产管理系统工作可靠性的重要问题。本文提出了基于分数阶偏微分的井下移动设备检测数据融合算法,并通过0.5阶偏微分方程在采煤机工作时液压油箱中油温检测数据融合处理实验中的应用,验证了该算法对于多重因素影响下的井下移动设备的检测数据具备较高的融合精度,可有效提高井下移动设备监测信息的检测精度和生产管理系统的工作可靠性。

Aimed at the important problems that when the Wireless Sensor Network technology is applied to collect all kinds of detection data of underground mobile equipment in real time,because the position of the equipment is in dynamic change during operation,the detection data is affected by multiple factors such as the performance of the detection equipment,the working environment of the equipment and signal interference,which caused the detection data have unpredictable detection errors,it leads to the detection data collected by data management center can’t reflect the real monitoring information of underground mobile equipment,which directly affects the safety of equipment operation and the reliability of production management system.This paper presents a Data Fusion Algorithm for underground mobile equipment detection based on fractional partial differential,then through the application of 0.5 order Partial Differential Equation in the fusion processing experiment of oil temperature detection data in the hydraulic tank of shearer working,verified the algorithm has high fusion accuracy for the detection data of underground mobile equipment under the influence of multiple factors,which can improve the detection accuracy of underground mobile equipment monitoring information and the reliability of production management system effectively.