Predictive AI Models for Intelligent Robot Health Monitoring

Abstract

Intelligent robotic systems increasingly require predictive health monitoring to ensure reliability, safety, and operational efficiency. Traditional maintenance methods cannot accurately predict failures or estimate component lifespan. This paper proposes an AI-driven predictive health monitoring framework that integrates multi-sensor data, intelligent feature engineering, deep learning, anomaly detection, fault diagnosis, and Remaining Useful Life (RUL) estimation. Using data from vibration, temperature, motor current, torque, acoustic signals, batteries, and controller logs, the framework continuously assesses robot health and predicts failures in real time. It supports intelligent maintenance scheduling, resource optimization, and autonomous maintenance decisions through adaptive learning. The proposed approach improves fault detection accuracy, reduces downtime and maintenance costs, enhances robot reliability and productivity, and supports the development of self-aware robotic systems for Industry 4.0 and Industry 5.0 smart manufacturing environments.