文献类型：期刊文献

英文题名：Design of Multi-Information Fusion Based Intelligent Electrical Fire Detection System for Green Buildings

作者：Ren, Xiaogeng[1];Li, Chunwang[1];Ma, Xiaojun[1];Chen, Fuxiang[1];Wang, Haoyu[1];Sharma, Ashutosh[2];Gaba, Gurjot Singh[3];Masud, Mehedi[4]

第一作者：任晓耕

通讯作者：Sharma, A[1];Masud, M[2]

机构：[1]Beijing Union Univ, Coll Biochem Engn, Beijing 100023, Peoples R China;[2]Southern Fed Univ, Inst Comp Technol & Informat Secur, Rostov Na Donu 344006, Russia;[3]Lovely Profess Univ, Dept Elect & Elect Engn, Phagwara 144411, India;[4]Taif Univ, Coll Comp & Informat Technol, Dept Comp Sci, POB 11099, At Taif 21944, Saudi Arabia

第一机构：北京联合大学生物化学工程学院

通讯机构：[1]corresponding author), Southern Fed Univ, Inst Comp Technol & Informat Secur, Rostov Na Donu 344006, Russia;[2]corresponding author), Taif Univ, Coll Comp & Informat Technol, Dept Comp Sci, POB 11099, At Taif 21944, Saudi Arabia.

年份：2021

卷号：13

期号：6

外文期刊名：SUSTAINABILITY

收录：;Scopus(收录号：2-s2.0-85103516268);WOS:【SSCI(收录号:WOS:000645695200001)，SCI-EXPANDED(收录号:WOS:000645695200001)】；

基金：Taif University Researchers Supporting Project number (TURSP-2020/10), Taif University, Taif, Saudi Arabia.

语种：英文

外文关键词：electrical fire; multi-information fusion; fuzzy logic reasoning; neural network; green building

摘要：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.