International Journal of Mechatronics and Manufacturing Technology

Authors: Sravesh Chatterjee , Heena Sahab , Tribhuwan Rana

Abstract: Industrial robots are widely adopted in modern manufacturing systems due to their precision, flexibility and productivity. However, robot failures and unexpected downtime leads to production losses, safety risks and high maintenance cost. Traditional maintenance approaches such as reactive or preventive maintenance are not sufficient for complex robotic systems where faults can develop gradually and unpredictably. Artificial intelligence (AI) techniques provide advanced capability for robot fault diagnosis and predictive maintenance by learning patterns from sensor data and detecting anomalies before failure occurs. This paper reviews AI-based methods used in robot fault detection, classification and remaining useful life prediction. Machine learning, deep learning, signal processing and data-driven health monitoring approaches are discussed. Applications in industrial robots, collaborative robots and autonomous robotic systems are examined. Challenges such as data scarcity, model interpretability and real-time implementation are also analyzed. The review shows that AI-based predictive maintenance significantly improves reliability, reduces downtime and supports smart manufacturing. Future research directions include digital twins, edge AI and self-healing robotic systems.

Keywords: robot fault diagnosis, predictive maintenance, artificial intelligence, machine learning, industrial robots, condition monitoring, anomaly detection, smart manufacturing