Intelligent Fault Detection via Dilated Convolutional Neural Networks

Mohammad Azam Khan, Yong Hwa Kim, Jaegul Choo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

The energy industry is currently going through a rapid change. With the appearance of low-cost IoT sensors and storage devices, it has now become possible to get very detailed data from the electricity grid system to be used for further analysis. The coming of the big data era has made the analysis easier. At the same time, we need to establish a safe transmission and distribution facilities for reliable grid operation. In this work-in-progress paper, we contribute an exploration of deep learning approach for intelligent fault detection system. The method works directly on raw temporal signals without any handcrafted feature extraction process. Our proposed method can not only achieve about 100% classification accuracy on normal signals but also show good domain adaptation capability.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages729-731
Number of pages3
ISBN (Electronic)9781538636497
DOIs
Publication statusPublished - 2018 May 25
Event2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018 - Shanghai, China
Duration: 2018 Jan 152018 Jan 18

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018

Other

Other2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
CountryChina
CityShanghai
Period18/1/1518/1/18

Fingerprint

Fault detection
Feature extraction
Electricity
Neural networks
Sensors
Costs
Industry
Internet of things
Big data
Deep learning
Grid
Energy industry
Sensor

Keywords

  • Convolutional Neural Networks
  • Deep Neural Networks
  • Dilated Convolution
  • Domain Adaptation
  • Intelligent Asset Management

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

Cite this

Khan, M. A., Kim, Y. H., & Choo, J. (2018). Intelligent Fault Detection via Dilated Convolutional Neural Networks. In Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018 (pp. 729-731). [8367217] (Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigComp.2018.00137

Intelligent Fault Detection via Dilated Convolutional Neural Networks. / Khan, Mohammad Azam; Kim, Yong Hwa; Choo, Jaegul.

Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 729-731 8367217 (Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Khan, MA, Kim, YH & Choo, J 2018, Intelligent Fault Detection via Dilated Convolutional Neural Networks. in Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018., 8367217, Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018, Institute of Electrical and Electronics Engineers Inc., pp. 729-731, 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018, Shanghai, China, 18/1/15. https://doi.org/10.1109/BigComp.2018.00137
Khan MA, Kim YH, Choo J. Intelligent Fault Detection via Dilated Convolutional Neural Networks. In Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 729-731. 8367217. (Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018). https://doi.org/10.1109/BigComp.2018.00137
Khan, Mohammad Azam ; Kim, Yong Hwa ; Choo, Jaegul. / Intelligent Fault Detection via Dilated Convolutional Neural Networks. Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 729-731 (Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018).
@inproceedings{14b96a21a13348fb85279002f6b424bd,
title = "Intelligent Fault Detection via Dilated Convolutional Neural Networks",
abstract = "The energy industry is currently going through a rapid change. With the appearance of low-cost IoT sensors and storage devices, it has now become possible to get very detailed data from the electricity grid system to be used for further analysis. The coming of the big data era has made the analysis easier. At the same time, we need to establish a safe transmission and distribution facilities for reliable grid operation. In this work-in-progress paper, we contribute an exploration of deep learning approach for intelligent fault detection system. The method works directly on raw temporal signals without any handcrafted feature extraction process. Our proposed method can not only achieve about 100{\%} classification accuracy on normal signals but also show good domain adaptation capability.",
keywords = "Convolutional Neural Networks, Deep Neural Networks, Dilated Convolution, Domain Adaptation, Intelligent Asset Management",
author = "Khan, {Mohammad Azam} and Kim, {Yong Hwa} and Jaegul Choo",
year = "2018",
month = "5",
day = "25",
doi = "10.1109/BigComp.2018.00137",
language = "English",
series = "Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "729--731",
booktitle = "Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018",

}

TY - GEN

T1 - Intelligent Fault Detection via Dilated Convolutional Neural Networks

AU - Khan, Mohammad Azam

AU - Kim, Yong Hwa

AU - Choo, Jaegul

PY - 2018/5/25

Y1 - 2018/5/25

N2 - The energy industry is currently going through a rapid change. With the appearance of low-cost IoT sensors and storage devices, it has now become possible to get very detailed data from the electricity grid system to be used for further analysis. The coming of the big data era has made the analysis easier. At the same time, we need to establish a safe transmission and distribution facilities for reliable grid operation. In this work-in-progress paper, we contribute an exploration of deep learning approach for intelligent fault detection system. The method works directly on raw temporal signals without any handcrafted feature extraction process. Our proposed method can not only achieve about 100% classification accuracy on normal signals but also show good domain adaptation capability.

AB - The energy industry is currently going through a rapid change. With the appearance of low-cost IoT sensors and storage devices, it has now become possible to get very detailed data from the electricity grid system to be used for further analysis. The coming of the big data era has made the analysis easier. At the same time, we need to establish a safe transmission and distribution facilities for reliable grid operation. In this work-in-progress paper, we contribute an exploration of deep learning approach for intelligent fault detection system. The method works directly on raw temporal signals without any handcrafted feature extraction process. Our proposed method can not only achieve about 100% classification accuracy on normal signals but also show good domain adaptation capability.

KW - Convolutional Neural Networks

KW - Deep Neural Networks

KW - Dilated Convolution

KW - Domain Adaptation

KW - Intelligent Asset Management

UR - http://www.scopus.com/inward/record.url?scp=85048516317&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048516317&partnerID=8YFLogxK

U2 - 10.1109/BigComp.2018.00137

DO - 10.1109/BigComp.2018.00137

M3 - Conference contribution

AN - SCOPUS:85048516317

T3 - Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018

SP - 729

EP - 731

BT - Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -