Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Advancing Fault Detection in Building Automation Systems through Deep Learning
This study proposes a deep learning model utilizing the BACnet (Building Automation and Control Network) protocol for the real-time detection of mechanical faults and security vulnerabilities in building automation systems. Integrating various machine learning algorithms and outlier detection techniques, this model is capable of monitoring and learning anomaly patterns in real-time. The primary aim of this paper is to enhance the reliability and efficiency of buildings and industrial facilities, offering solutions applicable across diverse industries such as manufacturing, energy management, and smart grids. Our findings reveal that the developed algorithm detects mechanical faults and security vulnerabilities with an accuracy of 96%, indicating its potential to significantly improve the safety and efficiency of building automation systems. However, the full validation of the algorithm’s performance in various conditions and environments remains a challenge, and future research will explore methodologies to address these issues and further enhance performance. This research is expected to play a vital role in numerous fields, including productivity improvement, data security, and the prevention of human casualties.
Advancing Fault Detection in Building Automation Systems through Deep Learning
This study proposes a deep learning model utilizing the BACnet (Building Automation and Control Network) protocol for the real-time detection of mechanical faults and security vulnerabilities in building automation systems. Integrating various machine learning algorithms and outlier detection techniques, this model is capable of monitoring and learning anomaly patterns in real-time. The primary aim of this paper is to enhance the reliability and efficiency of buildings and industrial facilities, offering solutions applicable across diverse industries such as manufacturing, energy management, and smart grids. Our findings reveal that the developed algorithm detects mechanical faults and security vulnerabilities with an accuracy of 96%, indicating its potential to significantly improve the safety and efficiency of building automation systems. However, the full validation of the algorithm’s performance in various conditions and environments remains a challenge, and future research will explore methodologies to address these issues and further enhance performance. This research is expected to play a vital role in numerous fields, including productivity improvement, data security, and the prevention of human casualties.
Advancing Fault Detection in Building Automation Systems through Deep Learning
Woo-Hyun Choi (Autor:in) / Jung-Ho Lewe (Autor:in)
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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