Towards Online Health Monitoring of Robotic Arm

Abstract

Robot arms exhibit complex dynamic behaviours as their joints move at different angular speeds, acceleration, torques, and rotation at various angles. These operations differ from that of rotating machines, which often move at fixed continuous speeds. Since the majority of health monitoring strategies have been designed for the latter, it is a challenge to develop a reliable and intelligent health monitoring system for robots that addresses the non-stationary nature of their signals; often requiring synergy of instrumentation, analytical and information technologies with knowledge and experience in design, operation and maintenance. This article presents preliminary findings regarding the extraction of crucial components from data obtained from an industrial robot arm, with the ultimate goal of designing a multi-sensor measurement system for online health monitoring. This approach serves as an alternative to the conventional method that relies on vibration signal analysis for detecting anomalies and predicting remaining useful life (RUL), when integrated with machine learning techniques. The primary aim of the proposed system is the online identification of operational anomalies, deterioration, or damage that may adversely affect the arm's reliability and safety. To achieve this goal, the measurement system can employ wavelet analysis and decision trees to accurately track each joint of the robot arm during operation.