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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 real and detected average environmental parameters in fire. (a) flue gas dimming extent change; (b) ambient temperature change.
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sensors-15-17366-f017: The real and detected average environmental parameters in fire. (a) flue gas dimming extent change; (b) ambient temperature change.

Mentions: It is easy to understand that not each node expresses a wide gap, because some nodes have few faults. They have been included in the higher grades to the faults. In contrast, according to Figure 13, the 16th node with visible faults on the second floor can be selected to reveal how much the performance gap between the above three kinds of method is. Consequently, the true environmental parameters and the detected data, which are the combination of 1000 experiments, are shown in Figure 17. Then, it can be seen that the two modules with different functions have faults and particular characteristics, in which the gap between true and faulty nodes becomes larger and larger. They are both separated into initial, fast developing, and violent stages. One of them is the flue gas dimming extent change, at which the violent stage lasts longer than the other two. The other is the ambient temperature change, at which the development stage is the longest of all. Indeed, the next analyses about the detection at each stage can be expressed clearly. In addition, the communicaion status here is like in a normal enviroment, so it need not to be introduced again.


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 real and detected average environmental parameters in fire. (a) flue gas dimming extent change; (b) ambient temperature change.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-17366-f017: The real and detected average environmental parameters in fire. (a) flue gas dimming extent change; (b) ambient temperature change.
Mentions: It is easy to understand that not each node expresses a wide gap, because some nodes have few faults. They have been included in the higher grades to the faults. In contrast, according to Figure 13, the 16th node with visible faults on the second floor can be selected to reveal how much the performance gap between the above three kinds of method is. Consequently, the true environmental parameters and the detected data, which are the combination of 1000 experiments, are shown in Figure 17. Then, it can be seen that the two modules with different functions have faults and particular characteristics, in which the gap between true and faulty nodes becomes larger and larger. They are both separated into initial, fast developing, and violent stages. One of them is the flue gas dimming extent change, at which the violent stage lasts longer than the other two. The other is the ambient temperature change, at which the development stage is the longest of all. Indeed, the next analyses about the detection at each stage can be expressed clearly. In addition, the communicaion status here is like in a normal enviroment, so it need not to be introduced again.

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.