Prognostic and Health Management (PHM) tool for Robot Operating System (ROS)
This tool addresses the problem of predicting and preventing failures in robots, which is critical for sustaining autonomy and improving system performance for robot developers and operators.
This study introduces an open-source Prognostic and Health Management (PHM) tool for Robot Operating System (ROS) that uses a model-based methodology. The tool allows users to monitor robot health, estimate Remaining Useful Life (RUL), and calculate the probability of task completion (PoTC) by inputting relevant equations, component information, and sensory data.
Nowadays, prognostics-aware systems are increasingly used in many systems and it is critical for sustaining autonomy. All engineering systems, especially robots, are not perfect. Absence of failures in a certain time is the perfect system and it is impossible practically. In all engineering works, we must try to predict or minimize/prevent failures in the system. Failures in the systems are generally unknown, so prediction of these failures and reliability of the system is made by prediction process. Reliability analysis is important for the improving the system performance, extending system lifetime, etc. Prognostic and Health Management (PHM) includes reliability, safety, predictive fault detection / isolation, advanced diagnostics / prognostics, component lifecycle tracking, health reporting and information management, etc. This study proposes an open source robot prognostic and health management tool using model-based methodology namely "Prognostics and Health Management tool for ROS". This tool is a generic tool for using with any kind of robot (mobile robot, robot arm, drone etc.) with compatible with ROS. Some features of this tool are managing / monitoring robots' health, RUL, probability of task completion (PoTC) etc. User is able to enter the necessary equations and components information (hazard rates, robot configuration etc.) to the PHM tool and the other sensory data like temperature, humidity, pressure, load etc. In addition to these, a case study is conducted for the mobile robots (OTA) using this tool.