Data analytics for predictive maintenance of industrial robots

The predictive maintenance of industrial machines is one of the challenging applications in the new era of Industry 4.0. Thanks to the predictive capabilities offered by the emerging smart data analytics, data-driven approaches for condition monitoring are becoming widely used for early detection of anomalies on production machines. The aim of this paper is to provide insights on the predictive maintenance of industrial robots and the possibility of building a condition-monitoring system based on the data analysis of robot's power measurements. A predictive modeling approach is proposed to detect robot manipulator accuracy errors based on robot's current data analysis for predictive maintenance purposes. An experimental procedure is also carried out to oversee the correlation between the robot accuracy error and a set of extracted features from current time-series, and to evaluate the proposed predictive modeling. The obtained results are satisfactory and prove the feasibility of building a data-driven condition monitoring of robot manipulators using the electrical power time-series data analysis.