Optimized Real-Time Monitoring and Fault Diagnosis System for Industrial Robots with Integrated Sensor Data

Abstract

This study presents an optimized real-time monitoring and fault diagnosis system tailored for industrial robots that integrates sensor data for improved performance. Industrial robots, as complex interdisciplinary products, are central to the field of intelligent manufacturing. Understanding their failure modes is essential for the development of robust fault diagnosis systems. Using Failure Mode and Effects Analysis (FMEA), this study systematically identifies and evaluates potential failure conditions. We then present a sensor-based fault diagnosis system and review recent advances in fault diagnosis techniques tailored for industrial robots. The proposed methodology includes real-time environmental monitoring using video sensors and a fault diagnosis system using integrated sensor data. Experimental analysis demonstrates the effectiveness of the proposed method in detecting anomalies, with accuracies ranging from 99.23% to 99.45%. The robustness and applicability of the system in the various industrial environments are highlighted, underscoring its potential to improve the reliability, safety, and efficiency of industrial robot operations.