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Design of Multi-Information Fusion Based Intelligent Electrical Fire Detection System for Green Buildings

Author

Listed:
  • Xiaogeng Ren

    (College of Biochemical Engineering, Beijing Union University, Beijing 100023, China)

  • Chunwang Li

    (College of Biochemical Engineering, Beijing Union University, Beijing 100023, China)

  • Xiaojun Ma

    (College of Biochemical Engineering, Beijing Union University, Beijing 100023, China)

  • Fuxiang Chen

    (College of Biochemical Engineering, Beijing Union University, Beijing 100023, China)

  • Haoyu Wang

    (College of Biochemical Engineering, Beijing Union University, Beijing 100023, China)

  • Ashutosh Sharma

    (Institute of Computer Technology and Information Security, Southern Federal University, 344006 Rostov-on-Don, Russia)

  • Gurjot Singh Gaba

    (Department of Electronics & Electrical Engineering, Lovely Professional University, Phagwara 144411, India)

  • Mehedi Masud

    (Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

Abstract

Building management systems are costly for small- to medium-sized buildings. A massive volume of data is collected on different building contexts by the Internet of Things (IoT), which is then further monitored. This intelligence is integrated into building management systems (BMSs) for energy consumption management in a cost-effective manner. Electric fire safety is paramount in buildings, especially in hospitals. Facility managers focus on fire protection strategies and identify where system upgrades are needed to maintain existing technologies. Furthermore, BMSs in hospitals should minimize patient disruption and be immune to nuisance alarms. This paper proposes an intelligent detection technology for electric fires based on multi-information fusion for green buildings. The system model was established by using fuzzy logic reasoning. The extracted multi-information fusion was used to detect the arc fault, which often causes electrical fires in the low-voltage distribution system of green buildings. The reliability of the established multi-information fusion model was verified by simulation. Using fuzzy logic reasoning and the membership function in fuzzy set theory to solve the uncertain relationship between faults and symptoms is a widely applied method. In order to realize the early prediction and precise diagnosis of faults, a fuzzy reasoning system was applied to analyze the arcs causing electrical fires in the lines. In order to accurately identify the fault arcs that easily cause electrical fires in low-voltage distribution systems for building management, this paper introduces in detail a fault identification method based on multi-information fusion, which can consolidate the complementary advantages of different types of judgment. The results demonstrate that the multi-information fusion method reduces the deficiency of a single criterion in fault arc detection and prevents electrical fires in green buildings more comprehensively and accurately. For the real-time dataset, the data results are presented, showing disagreements among the testing methods.

Suggested Citation

  • Xiaogeng Ren & Chunwang Li & Xiaojun Ma & Fuxiang Chen & Haoyu Wang & Ashutosh Sharma & Gurjot Singh Gaba & Mehedi Masud, 2021. "Design of Multi-Information Fusion Based Intelligent Electrical Fire Detection System for Green Buildings," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3405-:d:520274
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    References listed on IDEAS

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    6. Ashutosh Sharma & Rajiv Kumar & Manar Wasif Abu Talib & Saurabh Srivastava & Razi Iqbal, 2019. "Network modelling and computation of quickest path for service-level agreements using bi-objective optimization," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
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    Cited by:

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    2. Tomáš Loveček & Lenka Straková & Katarína Kampová, 2021. "Modeling and Simulation as Tools to Increase the Protection of Critical Infrastructure and the Sustainability of the Provision of Essential Needs of Citizens," Sustainability, MDPI, vol. 13(11), pages 1-18, May.
    3. Abdallah E. Elwakeel & Yasser S. A. Mazrou & Aml A. Tantawy & Abdelaziz M. Okasha & Adel H. Elmetwalli & Salah Elsayed & Abeer H. Makhlouf, 2023. "Designing, Optimizing, and Validating a Low-Cost, Multi-Purpose, Automatic System-Based RGB Color Sensor for Sorting Fruits," Agriculture, MDPI, vol. 13(9), pages 1-19, September.
    4. Ashok Kumar Tripathi & Hemraj Saini & Geetanjali Rathee, 2022. "Futuristic Prediction of Missing Value Imputation Methods Using Extended ANN," International Journal of Business Analytics (IJBAN), IGI Global, vol. 9(3), pages 1-12, July.
    5. Xiaofei Huang & Vishal Jagota & Einer Espinoza-Muñoz & Judith Flores-Albornoz, 2022. "Tourist hot spots prediction model based on optimized neural network algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 63-71, March.

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