Robust Fault Diagnosis of SCARA Industrial Robot Manipulator

Abstract

Nowadays, robotic systems are being in increasingly demanding in many industrial activities. In order to achieve the maximal performance, complex nonlinear dynamic robotic systems were developed. However, as a consequence, the rate of component malfunctions augments with the complexity of systems. These malfunctions are called faults, which may appear in different parts of the system and can induce changes in the dynamic behaviour. This paper deals with fault diagnosis of a particular kind of industrial robots called selective compliance assembly robot arm (SCARA), where both parameter and measurement uncertainties are taken into account. Residuals and thresholds are generated using the quantitative model-based method. The inverse geometric model is used to find analytical solutions for joints angles and distances given the trajectory of the end effector. The presented geometric model is then used to derive the kinematic model. Using this kinematic model, the robot controller computes the necessary torque applied to each DC servomotor in order to move the robot from the current position to the next desired position. The proposed robust fault diagnosis scheme is then implemented for a SCARA manipulator and simulation results are presented in both normal and faulty situations.