A Methodology and Experimental Implementation for Industrial Robot Health Assessment via Torque Signature Analysis

This manuscript focuses on methodological and technological advances in the field of health assessment and predictive maintenance for industrial robots. We propose a non-intrusive methodology for industrial robot joint health assessment. Torque sensor data is used to create a digital signature given a defined trajectory and load combination. The signature of each individual robot is later used to diagnose mechanical deterioration. We prove the robustness and reliability of the methodology in a real industrial use case scenario. Then, an in depth mechanical inspection is carried out in order to identify the root cause of the failure diagnosed in this article. The proposed methodology is useful for medium and long term health assessment for industrial robots working in assembly lines, where years of almost uninterrupted work can cause irreversible damage.