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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.


Flowchart of the proposed method.
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sensors-15-17366-f001: Flowchart of the proposed method.

Mentions: Figure 1 displays the flowchart of the proposed method based on the AF-DHNN algorithm. Firstly, two features are extracted from the signals measured by sensors at different locations in a building. These features are the root mean square of the filtered signal (FRMS) and the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy node one (NSDS). Secondly, a fuzzy inference operator is established to detect the single-node status at each node. Reasonable membership functions and fuzzy rules are set up to map the actual state of a node and failure problems. At the same time, the pre-alarmed set of sensors with the heaviest damaged nodes is actualized. Thirdly, the fuzzy c-means method is adopted to find the center of various failure problems, and an adaptive clustering rating system is established. Finally, the original values are taken into DHNN with the diagnosis standard and iterations [25,26] after applying FCMA and the Sorting and Classification algorithm [27,28]. Then, nodes are classified according to their different fault conditions by clear fault reasons, number and location for convenient statistics and maintenance.


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)

Flowchart of the proposed method.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-17366-f001: Flowchart of the proposed method.
Mentions: Figure 1 displays the flowchart of the proposed method based on the AF-DHNN algorithm. Firstly, two features are extracted from the signals measured by sensors at different locations in a building. These features are the root mean square of the filtered signal (FRMS) and the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy node one (NSDS). Secondly, a fuzzy inference operator is established to detect the single-node status at each node. Reasonable membership functions and fuzzy rules are set up to map the actual state of a node and failure problems. At the same time, the pre-alarmed set of sensors with the heaviest damaged nodes is actualized. Thirdly, the fuzzy c-means method is adopted to find the center of various failure problems, and an adaptive clustering rating system is established. Finally, the original values are taken into DHNN with the diagnosis standard and iterations [25,26] after applying FCMA and the Sorting and Classification algorithm [27,28]. Then, nodes are classified according to their different fault conditions by clear fault reasons, number and location for convenient statistics and maintenance.

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.