Abstract: In the application of fault diagnosis technology of manufacturing equipment,the position of industrial robot is in a random change state at work.When using wireless network technology to realize real-time detection of its information,there are great differences between test data that affects the reliability of fault diagnosis system due to the differences between the positions of robots and the performance of detection equipment.Based on the study of calculus theory and its application characteristics in information processing,a mobile device detection data fusion processing model was established based on fractional partial differential,which was applied to the robot detection information data fusion processing and fault diagnosis experiment in manufacturing system.The research showed that the fractional partial differential operator had the dual functions of enhancing signal strength and improving the accuracy of detection data in the fusion processing of robot information data in manufacturing system,which could effectively improve the reliability of logistics robot fault diagnosis system.

Key words: manufacturing system, data fusion, fractional partial differential, industrial robot

摘要： 在移动设备的故障诊断中,检测仪器性能间的差异性会导致信息数据检测值间存在差异;位置的随机变化状态亦会导致检测信息传输过程中存在能量损失的差异。因此,工业机器人故障诊断过程中,信息数据会在设备性能和工作环境两种因素的共同作用下,呈现无规律可循的测量误差,影响故障诊断系统工作可靠性。本文在研究微积分理论及其在信息处理中应用特性的基础上,建立了基于分数阶偏微分的移动设备检测数据融合处理模型,并将其应用于制造系统中机器人检测信息数据的融合处理和故障诊断实验中。研究表明,分数阶偏微分算子在制造系统机器人信息数据的融合处理中,具有增强信号强度和提高检测数据精度的双重功能,可有效提高物流机器人故障诊断系统的可靠性。

关键词: 制造系统, 数据融合, 分数阶偏微分, 工业机器人