Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators
Hydrogenerators are strategic assets for power utilities. Their reliability and availability can lead to significant benefits. For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies. A significant part of generator diagnosis relies on Partial Dischar...
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| Vydané v: | IEEE access Ročník 8; s. 5438 - 5454 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2169-3536, 2169-3536 |
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| Abstract | Hydrogenerators are strategic assets for power utilities. Their reliability and availability can lead to significant benefits. For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies. A significant part of generator diagnosis relies on Partial Discharge (PD) measurements, because the main cause of hydrogenerator breakdown comes from failure of its high voltage stator, which is a major component of hydrogenerators. A study of all stator failure mechanisms reveals that more than 85% of them involve the presence of PD activity. PD signal can be detected from the lead of the hydrogenerator while it is running, thus allowing for on-line diagnosis. Hydro-Québec has been collecting more than 33 000 unlabeled PD measurement files over the last decades. Up to now, this diagnostic technique has been quantified based on global PD amplitudes and integrated PD energy irrespective of the source of the PD signal. Several PD sources exist and they all have different relative risk, but in order to recognize the nature of the PD, or its source, the judgement of experts is required. In this paper, we propose a new method based on visual data analysis to build a PD source classifier with a minimum of labeled data. A convolutional variational autoencoder has been used to help experts to visually select the best training data set in order to improve the performances of the PD source classifier. |
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| AbstractList | Hydrogenerators are strategic assets for power utilities. Their reliability and availability can lead to significant benefits. For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies. A significant part of generator diagnosis relies on Partial Discharge (PD) measurements, because the main cause of hydrogenerator breakdown comes from failure of its high voltage stator, which is a major component of hydrogenerators. A study of all stator failure mechanisms reveals that more than 85% of them involve the presence of PD activity. PD signal can be detected from the lead of the hydrogenerator while it is running, thus allowing for on-line diagnosis. Hydro-Québec has been collecting more than 33 000 unlabeled PD measurement files over the last decades. Up to now, this diagnostic technique has been quantified based on global PD amplitudes and integrated PD energy irrespective of the source of the PD signal. Several PD sources exist and they all have different relative risk, but in order to recognize the nature of the PD, or its source, the judgement of experts is required. In this paper, we propose a new method based on visual data analysis to build a PD source classifier with a minimum of labeled data. A convolutional variational autoencoder has been used to help experts to visually select the best training data set in order to improve the performances of the PD source classifier. Hydrogenerators are strategic assets for power utilities. Their reliability and availability can lead to significant benefits. For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies. A significant part of generator diagnosis relies on Partial Discharge (PD) measurements, because the main cause of hydrogenerator breakdown comes from failure of its high voltage stator, which is a major component of hydrogenerators. A study of all stator failure mechanisms reveals that more than 85 % of them involve the presence of PD activity. PD signal can be detected from the lead of the hydrogenerator while it is running, thus allowing for on-line diagnosis. Hydro-Québec has been collecting more than 33 000 unlabeled PD measurement files over the last decades. Up to now, this diagnostic technique has been quantified based on global PD amplitudes and integrated PD energy irrespective of the source of the PD signal. Several PD sources exist and they all have different relative risk, but in order to recognize the nature of the PD, or its source, the judgement of experts is required. In this paper, we propose a new method based on visual data analysis to build a PD source classifier with a minimum of labeled data. A convolutional variational autoencoder has been used to help experts to visually select the best training data set in order to improve the performances of the PD source classifier. |
| Author | Amyot, Normand Zemouri, Ryad Kokoko, Olivier Tahan, Souheil Antoine Levesque, Melanie Hudon, Claude |
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| Keywords | model interpretation feature extraction convolutional variational autoencoder Hydrogenerators diagnosis generative model data visualization partial discharges deep neural networks |
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| References | ref13 ref15 ref14 ref10 fanny (ref11) 2018; 135 im (ref27) 2015 ref16 ref19 zemouri (ref49) 2019 barrios (ref47) 2019; 12 luo (ref43) 2017; 10 bengio (ref26) 2014 ref50 settles (ref5) 2009 han (ref1) 2019 ref46 ref45 ref48 ref42 krogh (ref6) 0 ref41 ref44 blei (ref38) 2016 ref8 ref7 ref9 ref4 ref40 lévesque (ref3) 2017; 8 kingma (ref36) 2013 ref35 ref34 ref31 ref30 zeiler (ref18) 2014 ref33 ref32 b?aszczy?ski (ref12) 2018; 138 ref2 ref39 kingma (ref37) 2017 ref24 van der maaten (ref20) 2008; 9 ref23 ref22 ref21 ref28 ref29 selvaraju (ref17) 2016 mishra (ref25) 2017 |
| References_xml | – volume: 12 start-page: 2485 year: 2019 ident: ref47 article-title: Partial discharge classification using deep learning methods-Survey of recent progress publication-title: Energies doi: 10.3390/en12132485 – ident: ref2 doi: 10.1109/EIC.2009.5166352 – volume: 135 start-page: 60 year: 2018 ident: ref11 article-title: Deep learning for imbalance data classification using class expert generative adversarial network publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2018.08.150 – ident: ref30 doi: 10.1016/j.cageo.2019.04.006 – start-page: 818 year: 2014 ident: ref18 article-title: Visualizing and understanding convolutional networks publication-title: Vision Computer – ident: ref50 doi: 10.1007/s10462-016-9526-2 – ident: ref46 doi: 10.1016/S0003-2670(01)95359-0 – ident: ref48 doi: 10.3390/s18103512 – ident: ref15 doi: 10.1016/j.media.2017.07.005 – volume: 138 year: 2018 ident: ref12 article-title: Improving bagging ensembles for class imbalanced data by active learning publication-title: Advances in Feature Selection for Data and Pattern Recognition – volume: 10 start-page: 1694 year: 2017 ident: ref43 article-title: A review of online partial discharge measurement of large generators publication-title: Energies doi: 10.3390/en10111694 – year: 2013 ident: ref36 article-title: Auto-encoding variational Bayes publication-title: arXiv 1312 6114 – year: 2017 ident: ref37 article-title: Variational inference & deep learning: A new synthesis – ident: ref42 doi: 10.1049/joe.2018.0249 – year: 2017 ident: ref25 article-title: A generative model for zero shot learning using conditional variational autoencoders publication-title: arXiv 1709 00663 – ident: ref4 doi: 10.1016/j.ress.2018.07.006 – ident: ref33 doi: 10.1016/j.neucom.2019.03.013 – year: 2016 ident: ref17 article-title: Grad-CAM: Visual explanations from deep networks via gradient-based localization publication-title: arXiv 1610 02391 – ident: ref22 doi: 10.1137/18M1216134 – ident: ref21 doi: 10.1109/ACCESS.2019.2916648 – ident: ref34 doi: 10.1109/ACCESS.2019.2894764 – ident: ref29 doi: 10.1177/1475921718788299 – ident: ref32 doi: 10.1016/j.jprocont.2019.01.008 – year: 2016 ident: ref38 article-title: Variational inference: A review for statisticians publication-title: arXiv 1601 00670 – ident: ref23 doi: 10.1016/j.neunet.2019.05.003 – ident: ref28 doi: 10.1016/j.patcog.2018.12.015 – ident: ref41 doi: 10.1016/j.knosys.2018.09.005 – ident: ref19 doi: 10.1016/j.patcog.2016.11.008 – ident: ref16 doi: 10.3390/app9081526 – ident: ref7 doi: 10.1109/TNNLS.2018.2855446 – start-page: 231 year: 0 ident: ref6 article-title: Neural network ensembles, cross validation and active learning publication-title: Proc 7th Int Conf Neural Inf Process Syst (NIPS) – volume: 8 start-page: 7 year: 2017 ident: ref3 article-title: Improvement of a hydrogenerator prognostic model by using partial discharge measurement analysis publication-title: Proc Annu Conf Prognostics Health Manage Soc – year: 2009 ident: ref5 article-title: Active learning literature survey – year: 2019 ident: ref1 article-title: Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application publication-title: ISA Trans – ident: ref8 doi: 10.1109/ACCESS.2018.2890693 – year: 2014 ident: ref26 article-title: How auto-encoders could provide credit assignment in deep networks via target propagation publication-title: arXiv 1407 7906 – ident: ref13 doi: 10.1109/MLSP.2012.6349808 – ident: ref14 doi: 10.1109/TVCG.2019.2903943 – ident: ref24 doi: 10.1016/j.knosys.2019.01.017 – ident: ref40 doi: 10.1016/j.jprocont.2018.02.004 – ident: ref44 doi: 10.1109/TDEI.2005.1430399 – year: 2019 ident: ref49 article-title: A new growing pruning deep learning neural network algorithm (GP-DLNN) publication-title: Neural Comput Appl – ident: ref35 doi: 10.1109/ACCESS.2018.2848210 – ident: ref9 doi: 10.1007/s13748-016-0094-0 – year: 2015 ident: ref27 article-title: Denoising criterion for variational auto-encoding framework publication-title: arXiv 1511 06406 – ident: ref39 doi: 10.1016/j.compchemeng.2019.106515 – ident: ref45 doi: 10.1016/0169-7439(87)80084-9 – volume: 9 start-page: 2579 year: 2008 ident: ref20 article-title: Visualizing data using t-SNE publication-title: J Mach Learn Res – ident: ref31 doi: 10.1016/j.engappai.2019.04.013 – ident: ref10 doi: 10.1109/TKDE.2008.239 |
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| SubjectTerms | Classifiers Computer Science convolutional variational autoencoder Data analysis Data visualization deep neural networks Diagnosis Diagnostic systems Discharge Failure mechanisms feature extraction generative model Hydrogenerators Machine Learning Maintenance engineering model interpretation Partial discharges Stators Training Training data Utilities Visualization |
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| Title | Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators |
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