The integration of artificial intelligence (AI) and machine learning (ML) technologies into predictive maintenance (PdM) systems has revolutionized industrial machinery management, offering unprecedented opportunities to optimize operational efficiency, reduce downtime, and extend equipment lifespan. This research paper presents a comprehensive analysis of AI-powered predictive maintenance applications in industrial settings, examining the technological foundations, implementation strategies, performance comparisons, and future prospects of these systems. Through systematic review of literature published prior to 2021 and comparative analysis of various AI algorithms, this study demonstrates that AI-powered predictive maintenance systems can achieve up to 30% reduction in maintenance costs and 70% decrease in equipment downtime compared to traditional reactive maintenance approaches. The paper addresses six critical aspects: technological foundations, machine learning algorithms and techniques, implementation strategies, performance evaluation and comparison, challenges and limitations, and future directions. Key findings indicate that ensemble methods and deep learning approaches show superior performance in fault prediction accuracy, while IoT integration and edge computing enable real-time monitoring capabilities essential for modern industrial applications.
Anupama et al. (Fri,) studied this question.
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