scholarly journals CRACK DETECTION SYSTEM FOR CONCRETE SURFACE BASED ON DEEP CONVOLUTION NEURAL NETWORK

2017 ◽  
Vol 73 (2) ◽  
pp. I_189-I_198
Author(s):  
Yasutoshi NOMURA ◽  
Saki MURAO ◽  
Yukihiro SAKAGUCHI ◽  
Hitoshi FURUTA
Keyword(s):  
Neural Network ◽  
Crack Detection ◽  
Detection System ◽  
Concrete Surface ◽  
Convolution Neural Network ◽  
Deep Convolution Neural Network

scholarly journals Deep convolution neural network for crack detection on asphalt pavement

2019 ◽  
Vol 1349 ◽  
pp. 012020 ◽  
Author(s):  
N A M Yusof ◽  
A Ibrahim ◽  
M H M Noor ◽  
N M Tahir ◽  
N M Yusof ◽  
...  
Keyword(s):  
Neural Network ◽  
Crack Detection ◽  
Asphalt Pavement ◽  
Convolution Neural Network ◽  
Deep Convolution Neural Network

An improved deep convolution neural network for predicting the remaining useful life of rolling bearings

2020 ◽  
pp. 1-9
Author(s):  
Yiming Guo ◽  
Hui Zhang ◽  
Zhijie Xia ◽  
Chang Dong ◽  
Zhisheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Convolution Neural Network ◽  
Dual Channel ◽  
Time Domain Data ◽  
Channel Input ◽  
Deep Convolution Neural Network ◽  
Useful Life

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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