Simulation-Based Predictive Maintenance for Rotor Fault Diagnosis in Autonomous Robotic Systems Using Deep Learning Models

Autonomous robotic systems, particularly those involved in industrial applications, rely heavily on the performance of their mechanical components, with rotors being central to their operation. Any fault in these systems, such as rotor misalignment, imbalance, or detachment, can lead to catastrophic failures, operational downtime, and financial losses. Predictive maintenance (PdM) strategies, based on fault diagnosis and remaining useful life (RUL) prediction, are crucial for mitigating these risks, extending the lifespan of robotic platforms, and enhancing operational efficiency. This paper presents a simulation-based approach to fault diagnosis and RUL prediction for rotor systems in autonomous robots. We propose a novel methodology using deep learning techniques—specifically Convolutional Neural Networks (CNN) for fault classification and Long Short-Term Memory (LSTM) networks for RUL prediction. The system is trained on synthetic rotor fault data generated from a robust simulation environment that models various fault conditions, including rotor drop-off, misalignment, and imbalance. The results show that the CNN model can classify fault types with an accuracy of 95%, while the LSTM model predicts RUL with a mean absolute error (MAE) of 5.6 hours, demonstrating the effectiveness of deep learning in enhancing predictive maintenance strategies for robotic systems. This approach shows promise for real-world applications in autonomous robotics by enabling early detection of faults and improving the reliability and safety of robotic operations.