Model-Based Fault Detection for Series Elastic Actuators

Ensuring the safe and reliable operation of collaborative robots demands robust sensor diagnostics. This paper introduces a methodology for formulating model-based constraints tailored for sensor diagnostics, featuring analytical relationships extending across mechanical and electrical domains. While applicable to various robotic systems, the study specifically centers on a robotic joint employing a series elastic actuator. Three distinct constraints are imposed on the series elastic actuator: the Torsional Spring Constraint, Joint Dynamics Constraint, and Electrical Motor Constraint. Through a simulation example, we demonstrate the efficacy of the proposed model-based fault detection methodology. The study addresses two distinct types of sensor faults that may arise in the torque sensor of a robot joint, and delves into their respective detection methods. This insightful fault detection methodology is customizable and applicable across various components of robots, offering fault diagnostic and isolation capabilities. This research contributes valuable insights aimed at enhancing the diagnostic capabilities essential for the optimal performance of robotic manipulators in collaborative environments.