A Capacitive Particle-Analyzing Smoke Detector for Very Early Fire Detection

被引:1
|
作者
Wang, Boqiang [1 ,2 ]
Zhao, Xuezeng [1 ]
Zhang, Yiyong [2 ]
Song, Zigang [2 ]
Wang, Zhuogang [2 ]
机构
[1] Harbin Inst Technol, Sch Mechatron Engn, Harbin 150006, Peoples R China
[2] China State Shipbuilding Corp Ltd, Res Inst 703, Harbin, Peoples R China
关键词
extreme early fire detection; smoke concentration detection; capacitive detection; multiscale signal processing;
D O I
10.3390/s24051692
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Smoke detectors face the challenges of increasing accuracy, sensitivity, and high reliability in complex use environments to ensure the timeliness, accuracy, and reliability of very early fire detection. The improvement in and innovation of the principle and algorithm of smoke particle concentration detection provide an opportunity for the performance improvement in the detector. This study is a new refinement of the smoke concentration detection principle based on capacitive detection of cell structures, and detection signals are processed by a multiscale smoke particle concentration detection algorithm to calculate particle concentration. Through experiments, it is found that the detector provides effective detection of smoke particle concentrations ranging from 0 to 10% obs/m; moreover, the detector can detect smoke particles at parts per million (PPM) concentration levels (at 2 and 5 PPM), and the accuracy of the detector can reach at least the 0.5 PPM level. Furthermore, the detector can detect smoke particle concentrations at better than 1 PPM accuracy even in an environment with 6% obs/m oil gas particles, 7% obs/m large dust interference particles, or 8% obs/m small dust interference particles.
引用
收藏
页数:20
相关论文
共 45 条
  • [31] Rapid Early Fire Smoke Detection System Using Slope Fitting in Video Image Histogram
    Wang, Haifeng
    Zhang, Yi
    Fan, Xin
    FIRE TECHNOLOGY, 2020, 56 (02) : 695 - 714
  • [32] Rapid Early Fire Smoke Detection System Using Slope Fitting in Video Image Histogram
    Haifeng Wang
    Yi Zhang
    Xin Fan
    Fire Technology, 2020, 56 : 695 - 714
  • [33] A satellite imagery smoke detection framework based on the Mahalanobis distance for early fire identification and positioning
    Sun, Yehan
    Jiang, Lijun
    Pan, Jun
    Sheng, Shiting
    Hao, Libo
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 118
  • [34] A Novel Single Shot-multibox Detector Based on Multiple Gaussian Mixture Model for Urban Fire Smoke Detection
    Han, Hao
    COMPUTER SCIENCE AND INFORMATION SYSTEMS, 2023, 20 (04) : 1819 - 1843
  • [35] Smoke Detection Using rGO-Coated eFBG Sensor for Early Warning of Coal Fire in Mines
    Shadab, Azhar
    Ansari, Md Tauseef Iqbal
    Raghuwanshi, Sanjeev Kumar
    Kumar, Santosh
    IEEE SENSORS JOURNAL, 2023, 23 (03) : 2153 - 2160
  • [36] An Intelligent Automatic Early Detection System of Forest Fire Smoke Signatures using Gaussian Mixture Model
    Yoon, Seok-Hwan
    Min, Joonyoung
    JOURNAL OF INFORMATION PROCESSING SYSTEMS, 2013, 9 (04): : 621 - 632
  • [37] An integrated approach for early forest fire detection and verification using optical smoke, gas and microwave sensors
    von Wahl, N.
    Heinen, S.
    Essen, H.
    Kruell, W.
    Tobera, R.
    Willms, I.
    MODELLING, MONITORING AND MANAGEMENT OF FOREST FIRES II, 2010, 137 : 97 - +
  • [38] UFS-Net: A unified flame and smoke detection method for early detection of fire in video surveillance applications using CNNs
    Hosseini, Ali
    Hashemzadeh, Mahdi
    Farajzadeh, Nacer
    JOURNAL OF COMPUTATIONAL SCIENCE, 2022, 61
  • [39] Optimization of Smoke-Detector Installation Location Based on Effect of Fan Equipment inside Distribution Panel on Fire Detection Performance
    Gu, In-Mo
    Yeon, Yeong-Mo
    Ryu, Dong-Seok
    Kim, Seung-Hee
    FIRE-SWITZERLAND, 2023, 6 (02):
  • [40] Vision-based Early Fire and Smoke Detection for Smart Factory Applications Using FFS-YOLO
    Phan, Duc Tri
    Yap, Kim-Hui
    Garg, Kratika
    Han, Boon Siew
    2023 IEEE 25TH INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING, MMSP, 2023,