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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    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. 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.
    3. 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.
    4. 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.

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