An Efficient and Flexible Diagnostic Method for Machinery Fault Detection Based on Convolutional Neural Network

2020 ◽  
pp. 380-388
Author(s):  
Geng Wang ◽  
Baolong Guo ◽  
Cheng Li ◽  
Zhe Huang ◽  
Jie Hu
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Diagnostic Method

ArcNet: Series AC Arc Fault Detection Based on Raw Current and Convolutional Neural Network

2021 ◽  
pp. 1-1
Author(s):  
Yao Wang ◽  
Linming Hou ◽  
Kamal Chandra Paul ◽  
Yunsheng Ban ◽  
Chen Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Ac Arc

Efficient-ArcNet: Series AC Arc Fault Detection using Lightweight Convolutional Neural Network

2021 ◽  
Author(s):  
Kamal Chandra Paul ◽  
Tiefu Zhao ◽  
Chen Chen ◽  
Yunsheng Ban ◽  
Yao Wang
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Ac Arc

scholarly journals Seismic fault detection using an encoder–decoder convolutional neural network with a small training set

2019 ◽  
Vol 16 (1) ◽  
pp. 175-189 ◽  
Author(s):  
Shengrong Li ◽  
Changchun Yang ◽  
Hui Sun ◽  
Hao Zhang
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Training Set ◽  
Seismic Fault

Fault detection and diagnosis for reactive distillation based on convolutional neural network

2021 ◽  
Vol 145 ◽  
pp. 107172
Author(s):  
Xiaolong Ge ◽  
Beibei Wang ◽  
Xinchuang Yang ◽  
Yu Pan ◽  
Botan Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Reactive Distillation ◽  
Fault Detection And Diagnosis ◽  
Detection And Diagnosis

scholarly journals Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2149 ◽  
Author(s):  
Jiao Liu ◽  
Jinfu Liu ◽  
Daren Yu ◽  
Myeongsu Kang ◽  
Weizhong Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Gas Turbine ◽  
Detection Sensitivity ◽  
Large Temperature ◽  
Gas Temperature ◽  
Ambient Conditions ◽  
Real World Data ◽  
The Impact

Gas turbine hot component failures often cause catastrophic consequences. Fault detection can improve the availability and economy of hot components. The exhaust gas temperature (EGT) profile is usually used to monitor the performance of the hot components. The EGT profile is uniform when the hot component is healthy, whereas hot component faults lead to large temperature differences between different EGT values. The EGT profile swirl under different operating and ambient conditions also cause temperature differences. Therefore, the influence of EGT profile swirl on EGT values must be eliminated. To improve the detection sensitivity, this paper develops a fault detection method for hot components based on a convolutional neural network (CNN). This paper demonstrates that a CNN can extract the information between adjacent EGT values and consider the impact of the EGT profile swirl. This paper reveals, in principle, that a CNN is a viable solution for dealing with fault detection for hot components. Based on the distribution characteristics of EGT thermocouples, the circular padding method is developed in the CNN. The sensitivity of the developed method is verified by real-world data. Moreover, the developed method is visualized in detail. The visualization results reveal that the CNN effectively considers the influence of the EGT profile swirl.

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