Stanford Database Library of American Journal of Applied Science and Technology

Vol. 5 No. 09 (2025)
Articles

Advanced Predictive Maintenance and Smart Indoor Air Quality Management in HVAC Systems: Integrating IoT, Machine Learning, and Industry 4.0 Frameworks

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  • Alexander M. Reynolds
Alexander M. Reynolds
Department of Mechanical Engineering, University of Toronto, Canada

Published 2025-09-30

Keywords

  • HVAC systems,
  • predictive maintenance,
  • indoor air quality,
  • IoT

How to Cite

Alexander M. Reynolds. (2025). Advanced Predictive Maintenance and Smart Indoor Air Quality Management in HVAC Systems: Integrating IoT, Machine Learning, and Industry 4.0 Frameworks. Stanford Database Library of American Journal of Applied Science and Technology, 5(09), 132–135. Retrieved from https://oscarpubhouse.com/index.php/sdlajast/article/view/67

Copyright (c) 2025 Alexander M. Reynolds

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The increasing complexity of modern buildings, coupled with growing demands for energy efficiency, occupant comfort, and environmental sustainability, has driven the development of advanced heating, ventilation, and air conditioning (HVAC) systems integrated with predictive maintenance and smart monitoring technologies. This paper presents a comprehensive exploration of predictive maintenance methodologies, machine learning applications, and Internet of Things (IoT)-enabled strategies for optimizing indoor air quality (IAQ) in contemporary buildings. The study synthesizes recent advances in low-cost sensing, digital twins, and Industry 4.0 frameworks, highlighting how these innovations can improve system reliability, minimize energy consumption, and enhance occupant well-being. By systematically reviewing the current state of research, identifying overlooked challenges, and analyzing emerging technologies, the paper establishes a conceptual framework for implementing Maintenance 4.0 in HVAC systems. Methodologically, the study draws upon a meta-analysis of machine learning algorithms, predictive maintenance strategies, and IoT deployments, integrating findings from diverse domains such as data mining, energy management, and thermal comfort optimization. Results reveal that combining predictive analytics with IoT-enabled monitoring and digital twin simulations significantly improves failure prediction accuracy, reduces unscheduled downtime, and supports proactive operational strategies. The discussion emphasizes theoretical implications, operational limitations, and potential pathways for future research, including AI-driven control strategies, multi-sensor fusion, and scalable deployment in smart buildings. Overall, the research demonstrates that the synergy between advanced predictive maintenance, data-driven AI methodologies, and IoT-integrated HVAC systems represents a transformative approach to sustainable building operations.

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