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

Jin S, Cui W, Jin Z, Wang Y - Sensors (Basel) (2015)

Bottom Line: 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 Article: PubMed Central - PubMed

Affiliation: School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China. Shanye2006@163.com.

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.

No MeSH data available.


The output from AF-DHNN. (a) No.1 node’s status; (b) all nodes’ status.
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sensors-15-17366-f013: The output from AF-DHNN. (a) No.1 node’s status; (b) all nodes’ status.

Mentions: As shown in Figure 13, DHNN is established and improved with the fuzzy status and diagnosis grading system of nodes and modules after 27 iterations. Besides, one of the 100 matrixes stands as a node’s status. In a matrix, the grade of flue gas dimming extent, which is utilized to determine faults, is the first line, temperature in the second, and the communication in the last. Also, the higher grades are on the left, the lower are on the right. Consequently, 11 faults of nodes from the conclusions of the fuzzy inference operator are found in the maintenance range by the AF-DHNN algorithm. It is convenient to test and repair the nodes with faults from the lowest grade to the higher.


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

Jin S, Cui W, Jin Z, Wang Y - Sensors (Basel) (2015)

The output from AF-DHNN. (a) No.1 node’s status; (b) all nodes’ status.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541939&req=5

sensors-15-17366-f013: The output from AF-DHNN. (a) No.1 node’s status; (b) all nodes’ status.
Mentions: As shown in Figure 13, DHNN is established and improved with the fuzzy status and diagnosis grading system of nodes and modules after 27 iterations. Besides, one of the 100 matrixes stands as a node’s status. In a matrix, the grade of flue gas dimming extent, which is utilized to determine faults, is the first line, temperature in the second, and the communication in the last. Also, the higher grades are on the left, the lower are on the right. Consequently, 11 faults of nodes from the conclusions of the fuzzy inference operator are found in the maintenance range by the AF-DHNN algorithm. It is convenient to test and repair the nodes with faults from the lowest grade to the higher.

Bottom Line: 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 Article: PubMed Central - PubMed

Affiliation: School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China. Shanye2006@163.com.

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.

No MeSH data available.