AI for Predictive Maintenance in Engineering Systems

Abstract

This comprehensive article examines the transformative impact of artificial intelligence on predictive maintenance strategies across various engineering systems, highlighting the evolution from traditional, reactive, and scheduled maintenance approaches toward sophisticated, data-driven methodologies. The article explores core technologies, including vibration analysis, thermal imaging, and sensor fusion techniques, that enable machine learning algorithms to detect equipment anomalies and predict failures with unprecedented accuracy. Through detailed examination of applications spanning industrial manufacturing, aviation, power generation, and transportation infrastructure, this article demonstrates how AI-enabled predictive maintenance systems significantly enhance operational reliability while reducing costs and improving safety outcomes. The article identifies key implementation methodologies, including anomaly detection algorithms, neural networks for pattern recognition, and real-time analytics platforms that process streaming sensor data to enable proactive maintenance decisions. While acknowledging substantial benefits such as extended equipment lifespan, optimized maintenance resource allocation, and enhanced safety performance, the article also addresses critical implementation challenges, including data quality issues, organizational change management requirements, and technical integration complexities with existing systems. The article reveals emerging trends toward autonomous maintenance systems, digital twin integration, and blockchain applications for data integrity, while identifying significant research opportunities in human-AI collaboration and ethical AI implementation. The article concludes that AI-driven predictive maintenance represents a paradigm shift that fundamentally alters equipment management practices across engineering disciplines, establishing new standards for operational excellence while addressing evolving sustainability and regulatory requirements in modern industrial environments.