The maintenance of industrial robots is a critical issue in manufacturing processes, and it can have a significant impact on productivity, downtime, and costs. To minimize the risks of failures and downtimes, predictive maintenance is becoming a popular approach in industry. Predictive maintenance aims to anticipate equipment failures before they occur, allowing for scheduled maintenance and reducing unexpected downtime.
Predictive maintenance is a crucial aspect of modern manufacturing processes, and the development of a predictive maintenance system for industrial robots can significantly improve productivity and reduce downtime. By analyzing the power measurements of the robot and using machine learning algorithms, the proposed system can identify accuracy errors and predict potential failures. This has the potential to provide valuable insights into the predictive maintenance of industrial robots and improve the overall efficiency of manufacturing processes.