TY - GEN
T1 - A Proposed Maintenance 4.0 Model for Laboratory Ventilation Systems
T2 - 2024 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2024
AU - Abdullah, Ammar Yaser
AU - Salem, Hossam Eldin
AU - Al Ameri, Hazza Muhsen Abdoul Qader
AU - Alnahdi, Mansoor Mohammed
AU - Okasha, Mohamed
AU - Shaban, Ibrahim Abdelfadeel
N1 - Publisher Copyright:
©2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Air quality plays a crucial role in the health and well-being of workers, with poor conditions potentially leading to respiratory issues, headaches, fatigue, and decreased productivity. This study utilizes Industry 4.0 configuration to assess air quality in laboratory settings and develop an online maintenance planning model for ventilation systems, termed Maintenance 4.0. In a laboratory at UAE University, air quality monitoring devices were installed to measure parameters such as CO2 emissions, humidity, PM1, PM2.5, PM10, and temperature. Several assessment procedures were employed to enhance the evaluation of ventilation system performance, referencing standards like ASHRAE 62.1, the World Health Organization (WHO) Air Quality Guidelines, and the Environmental Protection Agency (EPA) regulations. Additionally, predictive models were created using the collected data: one to forecast future air quality based on historical trends, and another—a Vector Autoregression (VAR) time series model—to predict air quality for the next 20 readings. The findings provide valuable insights into the current state of laboratory air quality and support the development of improvement strategies. By assessing ventilation performance and suggesting optimal maintenance times, this research benefits laboratory managers, maintenance personnel, and workers, enabling proactive measures through accurate air quality predictions and ultimately enhancing safety and productivity in laboratory environments.
AB - Air quality plays a crucial role in the health and well-being of workers, with poor conditions potentially leading to respiratory issues, headaches, fatigue, and decreased productivity. This study utilizes Industry 4.0 configuration to assess air quality in laboratory settings and develop an online maintenance planning model for ventilation systems, termed Maintenance 4.0. In a laboratory at UAE University, air quality monitoring devices were installed to measure parameters such as CO2 emissions, humidity, PM1, PM2.5, PM10, and temperature. Several assessment procedures were employed to enhance the evaluation of ventilation system performance, referencing standards like ASHRAE 62.1, the World Health Organization (WHO) Air Quality Guidelines, and the Environmental Protection Agency (EPA) regulations. Additionally, predictive models were created using the collected data: one to forecast future air quality based on historical trends, and another—a Vector Autoregression (VAR) time series model—to predict air quality for the next 20 readings. The findings provide valuable insights into the current state of laboratory air quality and support the development of improvement strategies. By assessing ventilation performance and suggesting optimal maintenance times, this research benefits laboratory managers, maintenance personnel, and workers, enabling proactive measures through accurate air quality predictions and ultimately enhancing safety and productivity in laboratory environments.
KW - Air quality index (AQI)
KW - Indoor air quality (IAQ)
KW - Industry 4.0
KW - Internet of Things (IoT)
KW - Machine learning (ML)
KW - Maintenance 4.0
UR - http://www.scopus.com/inward/record.url?scp=85217282339&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85217282339&partnerID=8YFLogxK
U2 - 10.1109/GCAIOT63427.2024.10833527
DO - 10.1109/GCAIOT63427.2024.10833527
M3 - Conference contribution
AN - SCOPUS:85217282339
T3 - 2024 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2024
BT - 2024 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 November 2024 through 21 November 2024
ER -