Fire Detection Method Based on Improved Deep Convolution Neural Network

Author(s):  
Yiheng Cai ◽  
Yajun Guo ◽  
Yuanyuan Li ◽  
Hui Li ◽  
Jiaqi Liu
2018 ◽  
Vol 38 (7) ◽  
pp. 0712006
Author(s):  
王文秀 Wang Wenxiu ◽  
傅雨田 Fu Yutian ◽  
董峰 Dong Feng ◽  
李锋 Li Feng

Author(s):  
Yan Wang ◽  
Weijie Zhang

Aiming at the problem of low detection accuracy of traditional power insulator fault detection methods, a power insulator fault detection method based on deep convolution neural network is designed. For the training of deep convolution neural network, the fault detection of power insulator based on deep convolution neural network is realized by anchor design, loss function design, candidate region selection mechanism establishment and sharing convolution features. The experimental results show that the fault detection method of power insulator based on deep convolution neural network is more accurate than the traditional method, and the detection time is less.


2019 ◽  
Vol E102.D (11) ◽  
pp. 2272-2275
Author(s):  
Menghan JIA ◽  
Feiteng LI ◽  
Zhijian CHEN ◽  
Xiaoyan XIANG ◽  
Xiaolang YAN

2021 ◽  
Vol 1971 (1) ◽  
pp. 012081
Author(s):  
SHEN Mengmeng ◽  
WANG Yong ◽  
MA Jiaqi ◽  
LI Chuanguo ◽  
HE Liangbo ◽  
...  

Author(s):  
Yiming Guo ◽  
Hui Zhang ◽  
Zhijie Xia ◽  
Chang Dong ◽  
Zhisheng Zhang ◽  
...  

The rolling bearing is the crucial component in the rotating machinery. The degradation process monitoring and remaining useful life prediction of the bearing are necessary for the condition-based maintenance. The commonly used deep learning methods use the raw or processed time domain data as the input. However, the feature extracted by these approaches is insufficient and incomprehensive. To tackle this problem, this paper proposed an improved Deep Convolution Neural Network with the dual-channel input from the time and frequency domain in parallel. The proposed methodology consists of two stages: the incipient failure identification and the degradation process fitting. To verify the effectiveness of the method, the IEEE PHM 2012 dataset is adopted to compare the proposed method and other commonly used approaches. The results show that the improved Deep Convolution Neural Network can effectively describe the degradation process for the rolling bearing.


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