基于自组织临界理论的工业机器人故障分析

Fault Analysis of Industrial Robots Based on Self-Organized Critical Theory

工业机器人作为一种结构精密复杂的机电一体化系统,若可以及时准确的预测故障,提前进行检修处理,消除隐患,才能从根本上解决故障问题。在自组织临界理论(SOC理论)的基础上,借鉴其自组织演化模型思想,利用工业机器人故障的自组织临界性判定依据,建立工业机器人的自回归移动平均模型(ARMA模型),根据残差值分析、解释工业机器人故障,从而寻找到预防和减少故障的方法和途径。

Industrial robots,as a structurally sophisticated mechatronic system,if the fault can be predicted in a timely and accurate manner,the maintenance process can be carried out in advance,and the hidden dangers can be eliminated to fundamentally solve the fault problems. Based on the self-organized critical theory（ SOC theory）,this paper draws lessons from its self-organization evolution model and uses the self-organized criticality of industrial robot failure to establish an autoregressive moving average model（ ARMA model） for industrial robots. We would analyze and explain the faults of industrial robots according to the analysis of residual value,so as to find ways and means to prevent and reduce faults.