IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i18p6695-d913616.html
   My bibliography  Save this article

A Pipe Ultrasonic Guided Wave Signal Generation Network Suitable for Data Enhancement in Deep Learning: US-WGAN

Author

Listed:
  • Lisha Peng

    (State Key Laboratory of Power System, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Shisong Li

    (State Key Laboratory of Power System, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Hongyu Sun

    (State Key Laboratory of Power System, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Songling Huang

    (State Key Laboratory of Power System, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

Abstract

A network ultrasonic Wasserstein generative adversarial network (US-WGAN), which can generate ultrasonic guided wave signals, is proposed herein to solve the problem of insufficient datasets for pipe ultrasonic nondestructive testing based on deep neural networks. This network was trained with pre-enhanced and US-WGAN-enhanced datasets with 3000 epochs; the ultrasound signals generated by the US-WGAN were proved to be of high quality (peak signal-to-noise ratio scores in the range of 30–50 dB) and belong to the same population distribution as the original dataset. To verify the effectiveness of the US-WGAN, a fully connected neural network with seven layers was established, and the performances of the network after data enhancement using the US-WGAN and popular virtual defects were verified for the same network parameters and structures. The results show that adoption of the US-WGAN effectively suppresses the overfitting phenomenon while training the network and increases the dataset size, thereby improving the training and testing accuracies (>97%). Additionally, we noted that a simple, fully connected shallow neural network was sufficient for achieving high-accuracy defect classification using the US-WGAN data enhancement method.

Suggested Citation

  • Lisha Peng & Shisong Li & Hongyu Sun & Songling Huang, 2022. "A Pipe Ultrasonic Guided Wave Signal Generation Network Suitable for Data Enhancement in Deep Learning: US-WGAN," Energies, MDPI, vol. 15(18), pages 1-12, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6695-:d:913616
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/18/6695/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/18/6695/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Haihui Tan & Xiaofeng Zhang & Li Zhang & Tangfei Tao & Guanghua Xu, 2019. "Ultrasonic Guided Wave Phased Array Focusing Technology and Its Application to Defrosting Performance Improvement of Air-Source Heat Pumps," Energies, MDPI, vol. 12(16), pages 1-18, August.
    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. Songling Huang & Lisha Peng & Hongyu Sun & Shisong Li, 2023. "Deep Learning for Magnetic Flux Leakage Detection and Evaluation of Oil & Gas Pipelines: A Review," Energies, MDPI, vol. 16(3), pages 1-27, January.

    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. Fei Wang & Rijing Zhao & Wenming Xu & Dong Huang & Zhiguo Qu, 2021. "A Heater-Assisted Air Source Heat Pump Air Conditioner to Improve Thermal Comfort with Frost-Retarded Heating and Heat-Uninterrupted Defrosting," Energies, MDPI, vol. 14(9), pages 1-13, May.
    2. Xingwang Tang & Qin Guo & Ming Li & Mingzhe Jiang, 2020. "Heating Performance Characteristics of an Electric Vehicle Heat Pump Air Conditioning System Based on Exergy Analysis," Energies, MDPI, vol. 13(11), pages 1-14, June.

    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:jeners:v:15:y:2022:i:18:p:6695-:d:913616. 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.