Journal article
Mohammad Azam Khan, Yong-Hwa Kim, J. Choo
International Conference on Big Data and Smart Computing, 2018
International Conference on Big Data and Smart Computing, 2018
Semantic Scholar
DBLP
DOI
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Cite
APA
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Khan, M. A., Kim, Y.-H., & Choo, J. (2018). Intelligent Fault Detection via Dilated Convolutional Neural Networks. International Conference on Big Data and Smart Computing.
Chicago/Turabian
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Khan, Mohammad Azam, Yong-Hwa Kim, and J. Choo. “Intelligent Fault Detection via Dilated Convolutional Neural Networks.” International Conference on Big Data and Smart Computing (2018).
MLA
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Khan, Mohammad Azam, et al. “Intelligent Fault Detection via Dilated Convolutional Neural Networks.” International Conference on Big Data and Smart Computing, 2018.
BibTeX Click to copy
@article{mohammad2018a,
title = {Intelligent Fault Detection via Dilated Convolutional Neural Networks},
year = {2018},
journal = {International Conference on Big Data and Smart Computing},
author = {Khan, Mohammad Azam and Kim, Yong-Hwa and Choo, J.}
}
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.
