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

Xiaogeng Ren, Chunwang Li, Xiaojun Ma, Fuxiang Chen, Haoyu Wang, Ashutosh Sharma, Gurjot Singh Gaba and Mehedi Masud
Additional contact information
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

Sustainability, 2021, vol. 13, issue 6, 1-15

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.

Keywords: electrical fire; multi-information fusion; fuzzy logic reasoning; neural network; green building (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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