Prognostic and Health Management (PHM) tool for Robot Operating System (ROS)

TL;DR

This paper introduces an open source PHM tool for ROS that enables generic health monitoring and failure prediction for various robots, enhancing reliability and operational safety.

Contribution

It presents a novel, model-based PHM tool compatible with ROS, allowing customizable failure prediction and health management for diverse robotic platforms.

Findings

Successfully applied to mobile robots in case study

Enables real-time health monitoring and RUL estimation

Supports user-defined equations and sensor data integration

Abstract

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.