Intelligent Fault Detection via Dilated Convolutional Neural Networks

Distilling Knowledge From an Ensemble of Convolutional Neural Networks for Seismic Fault Detection

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2020.3034960 ◽

2020 ◽

pp. 1-5

Author(s):

Zirui Wang ◽

Bo Li ◽

Naihao Liu ◽

Bangyu Wu ◽

Xu Zhu

Keyword(s):

Neural Networks ◽

Fault Detection ◽

Convolutional Neural Networks ◽

Seismic Fault

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Fault detection and diagnosis for Air Handling Unit based on multiscale convolutional neural networks

Energy and Buildings ◽

10.1016/j.enbuild.2021.110795 ◽

2021 ◽

Vol 236 ◽

pp. 110795

Author(s):

Fanyong Cheng ◽

Wenjian Cai ◽

Xin Zhang ◽

Huanyue Liao ◽

Can Cui

Keyword(s):

Neural Networks ◽

Fault Detection ◽

Convolutional Neural Networks ◽

Fault Detection And Diagnosis ◽

Air Handling Unit ◽

Detection And Diagnosis

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Seismic Fault Detection Using Convolutional Neural Networks Trained on Synthetic Poststacked Amplitude Maps

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2018.2875836 ◽

2019 ◽

Vol 16 (3) ◽

pp. 352-356 ◽

Cited By ~ 2

Author(s):

Axelle Pochet ◽

Pedro H. B. Diniz ◽

Helio Lopes ◽

Marcelo Gattass

Keyword(s):

Neural Networks ◽

Fault Detection ◽

Convolutional Neural Networks ◽

Seismic Fault

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Model-Based Fault Detection and Diagnosis Using Convolutional Neural Networks in Heat Source System with Water Thermal Storage Tanks

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/238/1/012070 ◽

2019 ◽

Vol 238 ◽

pp. 012070

Author(s):

S. Miyata ◽

Y. Akashi ◽

J. Lim ◽

K. Tanaka ◽

S. Tanaka ◽

...

Keyword(s):

Neural Networks ◽

Fault Detection ◽

Heat Source ◽

Convolutional Neural Networks ◽

Thermal Storage ◽

Fault Detection And Diagnosis ◽

Storage Tanks ◽

Model Based ◽

Detection And Diagnosis

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Fault Detection and Diagnosis Using Self-Attentive Convolutional Neural Networks for Variable-Length Sensor Data in Semiconductor Manufacturing

IEEE Transactions on Semiconductor Manufacturing ◽

10.1109/tsm.2019.2917521 ◽

2019 ◽

Vol 32 (3) ◽

pp. 302-309 ◽

Cited By ~ 6

Author(s):

Eunji Kim ◽

Sungzoon Cho ◽

Byeongeon Lee ◽

Myoungsu Cho

Keyword(s):

Neural Networks ◽

Fault Detection ◽

Convolutional Neural Networks ◽

Semiconductor Manufacturing ◽

Fault Detection And Diagnosis ◽

Variable Length ◽

Sensor Data ◽

Detection And Diagnosis

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Application of simplified convolutional neural networks for initial stator winding fault detection of the PMSM drive using different raw signal data

IET Electric Power Applications ◽

10.1049/elp2.12066 ◽

2021 ◽

Author(s):

Maciej Skowron ◽

Teresa Orlowska‐Kowalska ◽

Czeslaw T. Kowalski

Keyword(s):

Neural Networks ◽

Fault Detection ◽

Convolutional Neural Networks ◽

Stator Winding

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Seismic fault detection using convolutional neural networks with focal loss

Computers & Geosciences ◽

10.1016/j.cageo.2021.104968 ◽

2021 ◽

pp. 104968

Author(s):

Xiao-Li Wei ◽

Chun-Xia Zhang ◽

Sang-Woon Kim ◽

Kai-Li Jing ◽

Yong-Jun Wang ◽

...

Keyword(s):

Neural Networks ◽

Fault Detection ◽

Convolutional Neural Networks ◽

Seismic Fault

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Fault Detection and Isolation in Industrial Networks using Graph Convolutional Neural Networks

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) ◽

10.1109/icphm.2019.8819403 ◽

2019 ◽

Cited By ~ 1

Author(s):

Hamed Khorasgani ◽

Arman Hasanzadeh ◽

Ahmed Farahat ◽

Chetan Gupta

Keyword(s):

Neural Networks ◽

Fault Detection ◽

Convolutional Neural Networks ◽

Fault Detection And Isolation ◽

Industrial Networks

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InsuDet: A Fault Detection Method for Insulators of Overhead Transmission Lines Using Convolutional Neural Networks

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2021.3120796 ◽

2021 ◽

pp. 1-1

Author(s):

Xingtuo Zhang ◽

Yiyi Zhang ◽

Jiefeng Liu ◽

Chaohai Zhang ◽

Xueyue Xue ◽

...

Keyword(s):

Neural Networks ◽

Fault Detection ◽

Convolutional Neural Networks ◽

Transmission Lines ◽

Detection Method ◽

Overhead Transmission Lines

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Road Fault Detection by Using Convolutional Neural Networks

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9188 ◽

2020 ◽

Vol 17 (8) ◽

pp. 3374-3377

Author(s):

K. Ashok Kumar ◽

Vamsi Pulikonda ◽

Narendarnath Sai

Keyword(s):

Neural Network ◽

Neural Networks ◽

Computer Vision ◽

Fault Detection ◽

Convolutional Neural Networks ◽

Identification Problem ◽

Vehicular Traffic ◽

Setup Cost ◽

Laser Imaging

Bad conditions of road due to the potholes are one of the major cause of road damage and accidents to vehicles. Recently, with the increase in pollution and vehicular traffic, most of roads are being filled with many small and large potholes in most of places in the country. Detecting potholes manually is a time-consuming task and labour-intensive task, automating this process which saves a lot of time and money. Hence, Many different methodologies have been implemented that is from reporting to authorities manually to the use of laser imaging. Though all of these techniques have some disadvantages like risk while detection, high setup cost. By using the concept of Convolu-tional neural networks (CNN), a computer vision-based method we easily can identify the limitations, using the concept of Neural network by processing the image and detecting the potholes saves a lot of time and money. Inputting the data to the model with the camera regularly to identify properly potholes, cracks. This is best options for automating bad-road identification problem.

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