PMID- 26193280
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20150929
LR  - 20181113
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 15
IP  - 7
DP  - 2015 Jul 17
TI  - AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for 
      Fire Detection.
PG  - 17366-96
LID - 10.3390/s150717366 [doi]
AB  - 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.
FAU - Jin, Shan
AU  - Jin S
AD  - School of Electronic Information Engineering, Tianjin University, Tianjin 300072, 
      China. Shanye2006@163.com.
AD  - Fire Brigade of Hexi District, Tianjin 300222, China. Shanye2006@163.com.
AD  - Guangxi Experiment Center of Information Science, Guilin 541004, China. 
      Shanye2006@163.com.
FAU - Cui, Wen
AU  - Cui W
AD  - School of Management, Tianjin Polytechnic University, Tianjin 300387, China. 
      CuiWen0911@163.com.
AD  - Nankai Hospital of Traditional Chinese Medicine, Tianjin 300102, China. 
      CuiWen0911@163.com.
FAU - Jin, Zhigang
AU  - Jin Z
AD  - School of Electronic Information Engineering, Tianjin University, Tianjin 300072, 
      China. Zgjin@tju.edu.cn.
AD  - Guangxi Experiment Center of Information Science, Guilin 541004, China. 
      Zgjin@tju.edu.cn.
FAU - Wang, Ying
AU  - Wang Y
AD  - School of Electronic Information Engineering, Tianjin University, Tianjin 300072, 
      China. hellohiten@126.com.
LA  - eng
PT  - Journal Article
DEP - 20150717
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
PMC - PMC4541939
OTO - NOTNLM
OT  - Discrete Hopfield Neural Network
OT  - adaptive
OT  - fire detection
OT  - fuzzy C-means algorithm
OT  - fuzzy inference
OT  - node fault diagnosis method
OT  - wireless sensor networks
EDAT- 2015/07/21 06:00
MHDA- 2015/07/21 06:01
PMCR- 2015/07/17
CRDT- 2015/07/21 06:00
PHST- 2015/04/20 00:00 [received]
PHST- 2015/07/13 00:00 [revised]
PHST- 2015/07/14 00:00 [accepted]
PHST- 2015/07/21 06:00 [entrez]
PHST- 2015/07/21 06:00 [pubmed]
PHST- 2015/07/21 06:01 [medline]
PHST- 2015/07/17 00:00 [pmc-release]
AID - s150717366 [pii]
AID - sensors-15-17366 [pii]
AID - 10.3390/s150717366 [doi]
PST - epublish
SO  - Sensors (Basel). 2015 Jul 17;15(7):17366-96. doi: 10.3390/s150717366.