scholarly journals Cognitive IoT: a case study of applying deep learning approach in fault diagnosis of sucker-rod pumping system

2016 ◽  
Author(s):  
Hangqi Zhao ◽  
◽  
Jian Wang ◽  
Peng Gao ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Learning Approach ◽  
Pumping System ◽  
Sucker Rod

scholarly journals Open Switch Fault Diagnosis Method for PWM Voltage Source Rectifier Based on Deep Learning Approach

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 66595-66608 ◽  
Author(s):  
Tiancheng Shi ◽  
Yigang He ◽  
Tao Wang ◽  
Bing Li
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Voltage Source ◽  
Learning Approach ◽  
Open Switch ◽  
Diagnosis Method

A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin

2020 ◽  
Vol 13 (3) ◽  
pp. 915-927 ◽  
Author(s):  
Dostdar Hussain ◽  
Tahir Hussain ◽  
Aftab Ahmed Khan ◽  
Syed Ali Asad Naqvi ◽  
Akhtar Jamil
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
River Basin ◽  
Time Series Prediction ◽  
Learning Approach

scholarly journals A deep learning approach for anomaly detection with industrial time series data: a refrigerators manufacturing case study

2019 ◽  
Vol 38 ◽  
pp. 233-240 ◽  
Author(s):  
Mattia Carletti ◽  
Chiara Masiero ◽  
Alessandro Beghi ◽  
Gian Antonio Susto
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Series Data ◽  
Learning Approach

scholarly journals A Comparative Study of Fault Diagnosis for Train Door System: Traditional versus Deep Learning Approaches

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5160 ◽  
Author(s):  
Seokju Ham ◽  
Seok-Youn Han ◽  
Seokgoo Kim ◽  
Hyung Jun Park ◽  
Kee-Jun Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Traditional Approach ◽  
Fault Classification ◽  
Learning Approach ◽  
Health State ◽  
Features Selection ◽  
Learning Approaches ◽  
Current Signal ◽  
K Nearest Neighbor

A fault diagnosis of a train door system is carried out using the motor current signal that operates the door. A test rig is prepared, in which various fault modes are examined by applying extreme conditions, as well as the natural and artificial wears of critical components. Two approaches are undertaken toward the fault classification for comparative purposes: one is the traditional feature-based method that requires several steps for the processing features such as signal segmentation, the extraction of time-domain features, selection by Fisher’s discrimination, and K-nearest neighbor. The other is the deep learning approach by employing the convolutional neural network (CNN) to skip the hand-crafted features extraction process. In the traditional approach, good accuracy is found only after the current signal is segmented into the three velocity regimes, which enhances the discrimination capability. In the CNN, superior accuracy is obtained even by the original raw signal, which is more convenient in terms of implementation. However, in view of practical applications, the traditional approach is more useful in that the features processing can be easily applied to assess the health state of each fault and monitor the progression over time in the real operation, which is not enabled by the deep learning approach.

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