基于置信规则库推理的焊接机器人减速机曲柄轴磨损故障检测方法

针对焊接机器人伺服电机转矩信号特征与RV(Rotate Vector)减速机曲柄轴磨损状态之间存在的非线性对应关系,设计一种基于置信规则库(BRB)的磨损故障检测方法。首先,BRB系统输入选取电机转矩均值和转矩导数的均值,输出设定为曲柄轴磨损故障等级,建立描述输入和输出之间映射关系的置信规则库。当在线获取输入特征信号后,利用证据推理(ER)算法将输入激活的置信规则进行融合,得到关于故障等级的信度分布,通过该分布评估曲柄轴所处的磨损程度。最后,利用某型号工业机器人获取的实测转矩数据对所提方法进行验证,表明所设计的BRB故障检测方法可以在很大程度上代替维修工程师实现故障的自动检测。

Crankshaft Wear Fault Detection Method for Welding Robot Reducer based on Belief Rule Base Inference

Aiming at the nonlinear relationship between the welding machine servo motor torque signals and the RV(Rotate Vector)reducer crankshaft wear states,a wear fault detection method based on belief rule base inference(BRB)is designed. Firstly,the inputs of BRB system are considered as the mean values of the motor torques and torque derivatives,the outputs are set as the crankshaft wear fault levels. As a result,a belief rule base describing the mapping relationship between the inputs and the outputs is established. After the input signals are online obtained,the evidential reasoning(ER)algorithm is used to fuse the belief rules activated by inputs to obtain a belief distribution about the fault levels,and the degree of the crankshaft wear is evaluated by the distribution. Finally,using the measured torque data to verify the proposed method,it shows that the designed BRB fault detection method can largely replace the maintenance engineer to realize the automatic detection of the faults.