Abstract
Abstract Nowadays, advanced industrial robots are increasingly used and gradually replacing human activities in smart manufacturing that requires high precision and high performance. During this process, a small deviation of a robot axis can lead to other axes drifts, and then significantly affects the product quality. Hence, this paper aims to present an effective approach to monitor and diagnose the origin position deviations of multi-axis robots. The proposed method uses the encoder measurements of each axis to extract features and build appropriate health indicators. These obtained health indicators are then injected into a Machine Learning classifier to localize the origin of the deviation, i.e which axis causes these drifts. Furthermore, the performance of this method is verified through a real industrial test bench, used for machining, that investigates various deviation severities in different axes of the robot.
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