TY - JOUR
T1 - Maintenance 4.0 for HVAC Systems
T2 - Addressing Implementation Challenges and Research Gaps
AU - Shaban, Ibrahim Abdelfadeel
AU - Salem, Hossam Eldin
AU - Abdullah, Ammar Yaser
AU - Ameri, Hazza Muhsen Abdoul Qader Al
AU - Alnahdi, Mansoor Mohammed
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/4
Y1 - 2025/4
N2 - Highlights: What are the main findings? There is a lack of research on integrating Industry 4.0 into ventilation system maintenance planning. Existing reviews cover sensors, AI/ML, and big data in HVAC, but not integrated Maintenance 4.0. What are the implications of the main findings? Effective ventilation is crucial for indoor air quality, health, and energy efficiency. AI-driven analytics enable proactive, data-driven maintenance for optimized schedules, failure prediction, and improved performance. This article explores the integration of Maintenance 4.0 into HVAC (heating, ventilation, and air conditioning) systems, highlighting its essential role within the framework of Industry 4.0. Maintenance 4.0 utilizes advanced technologies such as artificial intelligence and IoT sensing technologies. It also incorporates sophisticated data management techniques to transform maintenance strategies into HVAC and indoor ventilation systems. These innovations work together to enhance energy efficiency, air quality, and overall system performance. The paper provides an overview of various Maintenance 4.0 frameworks, discussing the role of IoT sensors in real-time monitoring of environmental conditions, equipment health, and energy consumption. It highlights how AI-driven analytics, supported by IoT data, enable predictive maintenance and fault detection. Additionally, the paper identifies key research gaps and challenges that hinder the widespread implementation of Maintenance 4.0, including issues related to data quality, model interpretability, system integration, and scalability. This paper also proposes solutions to address these challenges, such as advanced data management techniques, explainable AI models, robust system integration strategies, and user-centered design approaches. By addressing these research gaps, this paper aims to accelerate the adoption of Maintenance 4.0 in HVAC systems, contributing to more sustainable, efficient, and intelligent built environments.
AB - Highlights: What are the main findings? There is a lack of research on integrating Industry 4.0 into ventilation system maintenance planning. Existing reviews cover sensors, AI/ML, and big data in HVAC, but not integrated Maintenance 4.0. What are the implications of the main findings? Effective ventilation is crucial for indoor air quality, health, and energy efficiency. AI-driven analytics enable proactive, data-driven maintenance for optimized schedules, failure prediction, and improved performance. This article explores the integration of Maintenance 4.0 into HVAC (heating, ventilation, and air conditioning) systems, highlighting its essential role within the framework of Industry 4.0. Maintenance 4.0 utilizes advanced technologies such as artificial intelligence and IoT sensing technologies. It also incorporates sophisticated data management techniques to transform maintenance strategies into HVAC and indoor ventilation systems. These innovations work together to enhance energy efficiency, air quality, and overall system performance. The paper provides an overview of various Maintenance 4.0 frameworks, discussing the role of IoT sensors in real-time monitoring of environmental conditions, equipment health, and energy consumption. It highlights how AI-driven analytics, supported by IoT data, enable predictive maintenance and fault detection. Additionally, the paper identifies key research gaps and challenges that hinder the widespread implementation of Maintenance 4.0, including issues related to data quality, model interpretability, system integration, and scalability. This paper also proposes solutions to address these challenges, such as advanced data management techniques, explainable AI models, robust system integration strategies, and user-centered design approaches. By addressing these research gaps, this paper aims to accelerate the adoption of Maintenance 4.0 in HVAC systems, contributing to more sustainable, efficient, and intelligent built environments.
KW - indoor air quality (IAQ)
KW - internet of things (IoT)
KW - machine learning (ML)
KW - maintenance 4.0
KW - ventilation systems
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U2 - 10.3390/smartcities8020066
DO - 10.3390/smartcities8020066
M3 - Review article
AN - SCOPUS:105003685325
SN - 2624-6511
VL - 8
JO - Smart Cities
JF - Smart Cities
IS - 2
M1 - 66
ER -
