基于分数阶偏微分的离散制造系统检测数据融合算法

Fusion algorithm of discrete manufacturing system detection data based on fractional partial differential

针对离散制造系统中由于设备性能和工作环境的不同,致使通过物联网收集的生产数据存在较大差异,直接影响制造执行系统可靠性的重要问题,研究了基于分数阶偏微分方程的离散制造系统差异性数据融合算法。采用物联网技术统一收集与综合离散制造系统中各个数据终端的生产数据,用分数阶偏微分算法对所获取的差异性数据进行处理,从而实现检测数据的有效融合。实验结果表明:与同类方法相比,该方法总绝对误差和数据波动性较小,融合精度较高,对提高离散制造执行系统的可靠性具有重要意义。

Aiming at the big difference existed in collected product data with Internet of Things（IoT）caused by difference of equipment performance and circumstances in discrete manufacturing system,which directly affected the reliability of manufacturing execution system,the difference data fusion algorithm of discrete manufacturing execution system based on fractional partial differential equations was researched.By using the technology of IoT to collect and integrate the production data of various data terminal in discrete manufacturing system,and fractional partial differential equations was used to process it to realize the effective integration of detection data.The experimental results showed that the total absolute error and data volatile of the proposed method was less by comparing with other similar methods,which had important significance to improve the reliability of discrete manufacturing execution system.