Fault diagnosis of a selective compliance assembly robot arm manipulator based on the end joint motion analysis: Threshold algorithm and experiments

This paper presented a method to carry out fault diagnosis via analyzing the motion signals of a SCARA. To analyze the motion signals of the end joint of a selective compliance assembly robot arm (SCARA) and carry out fault diagnosis. A model parameter-based threshold algorithm is proposed in this study to improve the efficiency of the fault diagnosis on the end joint of a SCARA manipulator. The operation state of the robot is determined by comparing the speed curve of the end joint of the robot with the threshold using the proposed algorithm. Firstly, the threshold range of the system output is estimated using the speed observer constructed via parameter separation. Secondly, the acceleration signals of the end joint of the robot are collected at various operational angular speeds by a single acceleration sensor installed at the end joint of the manipulator. The operation state of the robot is evaluated by analyzing the trend and vibration characteristics of its acceleration. Finally, experiments are conducted at three different speeds: 2.4rad/s, 3.12rad/s and 3.6rad/s. Some robot malfunctions are detected by comparing the actual speed with the threshold. Thus, the proposed method can be used to monitor the variation signal in each robot joint through a single accelerometer mounted on the top of the manipulator.

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