Date of Defense
28-4-2025 4:00 PM
Location
Meeting Room 1043
Document Type
Thesis Defense
Degree Name
Master of Science in Mechanical Engineering (MSME)
College
COE
Department
Mechanical and Aerospace Engineering
First Advisor
Dr. Ibrahim Abdelfadeel Shaban
Keywords
Air quality, predictive modeling, IQ-Air sensor, ventilation system, indoor air pollution, real-time monitoring, predictive maintenance.
Abstract
This study introduces an advanced Maintenance 4.0 framework aimed at systematically evaluating and mitigating air quality risks within laboratory environments, specifically applied to the context of the United Arab Emirates University (UAEU). Employing real-time monitoring through sophisticated IQ-Air sensors, this research continuously captures key indoor environmental parameters, such as particulate matter concentrations (PM₁, PM₂.₅, PM₁₀), carbon dioxide (CO₂) levels, ambient temperature, and humidity. State-of-the-art machine learning algorithms are developed and operationalized to enhance ventilation system efficiencies and proactively forecast maintenance requirements, thereby transitioning from reactive to predictive maintenance strategies.
Empirical results indicate that Maintenance 4.0 applications significantly enhance the capacity to detect periods of compromised air quality and facilitate timely, data-driven maintenance actions. Additionally, integrating Industry 4.0 technological innovations, including the Internet of Things (IoT) and advanced big data analytics, demonstrably improves overall system efficacy, occupant health, and environmental sustainability. The proposed methodological framework is robust, scalable, and aligns effectively with internationally recognized standards and guidelines, specifically ASHRAE 62.1, the World Health Organization (WHO), and the Environmental Protection Agency (EPA). Consequently, this research offers substantial contributions
IMPLEMENTING MAINTENANCE 4.0 METHODS IN VENTILATION SYSTEMS: A CASE STUDY IN UAEU LABORATORIES
Meeting Room 1043
This study introduces an advanced Maintenance 4.0 framework aimed at systematically evaluating and mitigating air quality risks within laboratory environments, specifically applied to the context of the United Arab Emirates University (UAEU). Employing real-time monitoring through sophisticated IQ-Air sensors, this research continuously captures key indoor environmental parameters, such as particulate matter concentrations (PM₁, PM₂.₅, PM₁₀), carbon dioxide (CO₂) levels, ambient temperature, and humidity. State-of-the-art machine learning algorithms are developed and operationalized to enhance ventilation system efficiencies and proactively forecast maintenance requirements, thereby transitioning from reactive to predictive maintenance strategies.
Empirical results indicate that Maintenance 4.0 applications significantly enhance the capacity to detect periods of compromised air quality and facilitate timely, data-driven maintenance actions. Additionally, integrating Industry 4.0 technological innovations, including the Internet of Things (IoT) and advanced big data analytics, demonstrably improves overall system efficacy, occupant health, and environmental sustainability. The proposed methodological framework is robust, scalable, and aligns effectively with internationally recognized standards and guidelines, specifically ASHRAE 62.1, the World Health Organization (WHO), and the Environmental Protection Agency (EPA). Consequently, this research offers substantial contributions