IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i18p2336-d639639.html
   My bibliography  Save this article

Synthetic Data Augmentation and Deep Learning for the Fault Diagnosis of Rotating Machines

Author

Listed:
  • Asif Khan

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 gil, Jung-gu, Seoul 04620, Korea)

  • Hyunho Hwang

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 gil, Jung-gu, Seoul 04620, Korea)

  • Heung Soo Kim

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 gil, Jung-gu, Seoul 04620, Korea)

Abstract

As failures in rotating machines can have serious implications, the timely detection and diagnosis of faults in these machines is imperative for their smooth and safe operation. Although deep learning offers the advantage of autonomously learning the fault characteristics from the data, the data scarcity from different health states often limits its applicability to only binary classification (healthy or faulty). This work proposes synthetic data augmentation through virtual sensors for the deep learning-based fault diagnosis of a rotating machine with 42 different classes. The original and augmented data were processed in a transfer learning framework and through a deep learning model from scratch. The two-dimensional visualization of the feature space from the original and augmented data showed that the latter’s data clusters are more distinct than the former’s. The proposed data augmentation showed a 6–15% improvement in training accuracy, a 44–49% improvement in validation accuracy, an 86–98% decline in training loss, and a 91–98% decline in validation loss. The improved generalization through data augmentation was verified by a 39–58% improvement in the test accuracy.

Suggested Citation

  • Asif Khan & Hyunho Hwang & Heung Soo Kim, 2021. "Synthetic Data Augmentation and Deep Learning for the Fault Diagnosis of Rotating Machines," Mathematics, MDPI, vol. 9(18), pages 1-26, September.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2336-:d:639639
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/18/2336/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/18/2336/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brian Kenji Iwana & Seiichi Uchida, 2021. "An empirical survey of data augmentation for time series classification with neural networks," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-32, July.
    2. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    3. Xiang Li & Wei Zhang & Qian Ding & Jian-Qiao Sun, 2020. "Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 433-452, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fengyun Xie & Gan Wang & Jiandong Shang & Enguang Sun & Sanmao Xie, 2023. "Gearbox Fault Diagnosis Based on Multi-Sensor Deep Spatiotemporal Feature Representation," Mathematics, MDPI, vol. 11(12), pages 1-19, June.
    2. Pan Zheng & Wenqin Zhao & Yaqiong Lv & Lu Qian & Yifan Li, 2022. "Health Status-Based Predictive Maintenance Decision-Making via LSTM and Markov Decision Process," Mathematics, MDPI, vol. 11(1), pages 1-13, December.
    3. O-Jong Kim & Changdon Kee, 2023. "Wavelet and Neural Network-Based Multipath Detection for Precise Positioning Systems," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    4. Yeong Rim Noh & Salman Khalid & Heung Soo Kim & Seung-Kyum Choi, 2023. "Intelligent Fault Diagnosis of Robotic Strain Wave Gear Reducer Using Area-Metric-Based Sampling," Mathematics, MDPI, vol. 11(19), pages 1-22, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
    2. Fei Teng & Yafei Song & Xinpeng Guo, 2021. "Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model," Mathematics, MDPI, vol. 9(19), pages 1-21, September.
    3. V. Y. Kondaiah & B. Saravanan, 2022. "Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method," Energies, MDPI, vol. 15(14), pages 1-17, July.
    4. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    5. Shree Krishna Acharya & Young-Min Wi & Jaehee Lee, 2019. "Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation," Energies, MDPI, vol. 12(18), pages 1-19, September.
    6. Sepehr Moalem & Roya M. Ahari & Ghazanfar Shahgholian & Majid Moazzami & Seyed Mohammad Kazemi, 2022. "Long-Term Electricity Demand Forecasting in the Steel Complex Micro-Grid Electricity Supply Chain—A Coupled Approach," Energies, MDPI, vol. 15(21), pages 1-17, October.
    7. Bulent Haznedar & Huseyin Cagan Kilinc & Furkan Ozkan & Adem Yurtsever, 2023. "Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 681-701, May.
    8. Kanitta Yarak & Apichon Witayangkurn & Kunnaree Kritiyutanont & Chomchanok Arunplod & Ryosuke Shibasaki, 2021. "Oil Palm Tree Detection and Health Classification on High-Resolution Imagery Using Deep Learning," Agriculture, MDPI, vol. 11(2), pages 1-16, February.
    9. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    10. Zhong, Wei & Huang, Wei & Lin, Xiaojie & Li, Zhongbo & Zhou, Yi, 2020. "Research on data-driven identification and prediction of heat response time of urban centralized heating system," Energy, Elsevier, vol. 212(C).
    11. Song, Wanqing & Cattani, Carlo & Chi, Chi-Hung, 2020. "Multifractional Brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting: An integrated approach," Energy, Elsevier, vol. 194(C).
    12. Myoungsoo Kim & Wonik Choi & Youngjun Jeon & Ling Liu, 2019. "A Hybrid Neural Network Model for Power Demand Forecasting," Energies, MDPI, vol. 12(5), pages 1-17, March.
    13. Jiil Kim & Cheong Hee Park, 2020. "Partial Discharge Detection Based on Anomaly Pattern Detection," Energies, MDPI, vol. 13(20), pages 1-11, October.
    14. Wu, Jingyao & Zhao, Zhibin & Sun, Chuang & Yan, Ruqiang & Chen, Xuefeng, 2021. "Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    15. Odin Foldvik Eikeland & Filippo Maria Bianchi & Harry Apostoleris & Morten Hansen & Yu-Cheng Chiou & Matteo Chiesa, 2021. "Predicting Energy Demand in Semi-Remote Arctic Locations," Energies, MDPI, vol. 14(4), pages 1-17, February.
    16. Sholeh Hadi Pramono & Mahdin Rohmatillah & Eka Maulana & Rini Nur Hasanah & Fakhriy Hario, 2019. "Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System," Energies, MDPI, vol. 12(17), pages 1-16, August.
    17. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2021. "A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality," Energies, MDPI, vol. 14(19), pages 1-19, September.
    18. Gillmann, Niels & Kim, Alisa, 2021. "Quantification of Economic Uncertainty: a deep learning approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242421, Verein für Socialpolitik / German Economic Association.
    19. M. Nagaraju & Priyanka Chawla, 0. "Systematic review of deep learning techniques in plant disease detection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-14.
    20. Giacomo Talluri & Gabriele Maria Lozito & Francesco Grasso & Carlos Iturrino Garcia & Antonio Luchetta, 2021. "Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities," Energies, MDPI, vol. 14(24), pages 1-23, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2336-:d:639639. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.