AI/ML-Assisted Control and Predictive Maintenance for Drives

Abstract

Electric drives are core components in industrial automation, transportation systems, renewable energy plants, and smart manufacturing. Conventional control strategies such as PID and model-based control methods have been widely used for decades, but increasing system complexity and demand for higher efficiency and reliability require more advanced solutions. Artificial Intelligence (AI) and Machine Learning (ML) techniques are now being integrated with drive systems to enhance control performance and enable predictive maintenance. AI/ML-assisted control offers adaptive tuning, disturbance rejection, and non-linear modeling capabilities, while predictive maintenance approaches allow early fault detection, reduction of downtime, and improved asset life cycle management.

This paper presents a comprehensive review of AI/ML-assisted control strategies for electrical drives, including neural networks, fuzzy logic systems, reinforcement learning, and hybrid techniques. In addition, it discusses predictive maintenance methodologies based on data analytics, vibration analysis, current signature analysis, and deep learning models. The challenges, limitations, and future research directions are also examined. The integration of AI/ML in drive systems is shown to significantly improve performance, reliability, and cost efficiency, although issues such as data quality, computational cost, and implementation complexity still remain.

KEYWORDS: Artificial Intelligence, Machine Learning, Electric Drives, Predictive Maintenance, Neural Networks, Reinforcement Learning, Condition Monitoring, Fault Diagnosis