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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in March 2026 (Volume 29, Issue 3) Submit manuscript

AI-powered predictive maintenance for industrial machinery: A comprehensive analysis of machine learning applications and industrial implementation

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  • AI-powered predictive maintenance for industrial machinery: A comprehensive analysis of machine learning applications and industrial implementation

Anupama A 1, * Ramya S Yamikar 2 and Asiya Banu B 3

1 Department of Mechanical Engineering. Government Polytechnic, Chitradurga-577501, Karnataka, India.
2 Department of Computer Science Engineering, DRR Government Polytechnic, Davangere-577004, Karnataka, India. 
3 Department of computer Science Engineering, Government polytechnic, Harihara – 577601, Karnataka, India
 
Review Article
World Journal of Advanced Research and Reviews, 2022, 15(03), 656-665
Article DOI: 10.30574/wjarr.2022.15.3.0932
DOI url: https://doi.org/10.30574/wjarr.2022.15.3.0932
Received on 02 September 2022; revised on 10 September 2022; accepted on 22 September 2022
 
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.
 
Predictive Maintenance; Artificial Intelligence; Machine Learning; Industrial Machinery; Industry 4.0; IoT; Fault Detection
 
https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2022-0932.pdf

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Anupama A, Ramya S Yamikar and Asiya Banu B. AI-powered predictive maintenance for industrial machinery: A comprehensive analysis of machine learning applications and industrial implementation. World Journal of Advanced Research and Reviews, 2022, 15(3), 656-665. Article DOI: https://doi.org/10.30574/wjarr.2022.15.3.0932

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