Optimizing Reliability: A Comprehensive Review of AI-Driven Predictive Maintenance in Cyber-Physical Systems

Abstract

Predictive maintenance (PdM) in cyber-physical systems (CPS) is critical for ensuring optimal performance, minimizing downtime, and extending the lifespan of equipment. Integrating artificial intelligence (AI) techniques into PdM has brought transformative improvements, enabling more accurate failure predictions through data-driven models. This review paper explores the role of AI in predictive maintenance within CPS, focusing on machine learning, deep learning, and reinforcement learning approaches. It examines how these AI technologies are applied across various industries, including manufacturing, energy, transportation, and infrastructure, to improve system reliability and reduce maintenance costs. Additionally, the paper identifies the challenges of implementing AI in PdM, such as issues with data quality, scalability, and the interpretability of AI models, especially in safety-critical applications. The review also highlights the emerging trends in research, including hybrid AI models, the integration of AI with the Internet of Things (IoT) for real-time maintenance monitoring, and advancements in sustainability through AI-driven PdM. By providing a comprehensive overview of the current state and future directions in this field, this paper aims to offer valuable insights into the potential of AI to optimize the reliability and efficiency of CPS in diverse sectors.