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Sensors 2015, 15(7), 17366-17396; doi:10.3390/s150717366

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

1,2,5,* , 3,4
,
1,5,* and 1
1
School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China
2
Fire Brigade of Hexi District, Tianjin 300222, China
3
School of Management, Tianjin Polytechnic University, Tianjin 300387, China
4
Nankai Hospital of Traditional Chinese Medicine, Tianjin 300102, China
5
Guangxi Experiment Center of Information Science, Guilin 541004, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 20 April 2015 / Revised: 13 July 2015 / Accepted: 14 July 2015 / Published: 17 July 2015
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [1325 KB, uploaded 17 July 2015]   |  

Abstract

Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. View Full-Text
Keywords: wireless sensor networks; node fault diagnosis method; fire detection; adaptive; Discrete Hopfield Neural Network; fuzzy C-means algorithm; fuzzy inference wireless sensor networks; node fault diagnosis method; fire detection; adaptive; Discrete Hopfield Neural Network; fuzzy C-means algorithm; fuzzy inference
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Jin, S.; Cui, W.; Jin, Z.; Wang, Y. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection. Sensors 2015, 15, 17366-17396.

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