• DocumentCode
    2283080
  • Title

    A fire detecting method based on multi-sensor data fusion

  • Author

    Chen, Shaohua ; Bao, Hong ; Zeng, Xianyun ; Yang, Yimin

  • Author_Institution
    Fac. of Autom., Guangdong Univ. of Technol., China
  • Volume
    4
  • fYear
    2003
  • fDate
    5-8 Oct. 2003
  • Firstpage
    3775
  • Abstract
    In this paper, a fire detection system is designed based on multi-sensor data fusion technology. According to the fire signal\´s inherent character, the around temperature, smoke density and CO density are considered as the chief fire detecting signals. By introducing the "feedback" of cybernetics, a new algorithm that extracts the fire data-fitting characteristic is presented. The algorithm may improve the accuracy and quickness of early warning with a tendency feedback signal. In this fire detecting system, the fire experiential characteristic and the fire data-fitting characteristic of fire signal data are fused by the fuzzy inference system to get the last fire probability. Finally simulation experiments are done for typical flaming fire, typical smoldering fire and the fire under typical disturbance signals in kitchen environment. And satisfactory results are obtained.
  • Keywords
    alarm systems; array signal processing; artificial intelligence; feedback; fires; fuzzy neural nets; safety; sensor fusion; CO density; artificial intelligence; feedback signal; fire data-fitting characteristic; fire detecting method; fire experiential characteristic; fire probability; fuzzy inference system; multisensor data fusion; neural network; smoke density; Cybernetics; Data mining; Feedback; Fires; Fuzzy systems; Gas detectors; Inference algorithms; Signal detection; Smoke detectors; Temperature;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 2003. IEEE International Conference on
  • ISSN
    1062-922X
  • Print_ISBN
    0-7803-7952-7
  • Type

    conf

  • DOI
    10.1109/ICSMC.2003.1244476
  • Filename
    1244476