IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i17p5286-d622174.html
   My bibliography  Save this article

D-dCNN : A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

Author

Listed:
  • Ugochukwu Ejike Akpudo

    (Department of Mechanical Engineering, Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, 61 Daehak-ro (Yangho-Dong), Gumi 39177, Gyeongbuk, Korea)

  • Jang-Wook Hur

    (Department of Mechanical Engineering, Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, 61 Daehak-ro (Yangho-Dong), Gumi 39177, Gyeongbuk, Korea)

Abstract

This paper develops a novel hybrid feature learner and classifier for vibration-based fault detection and isolation (FDI) of industrial apartments. The trained model extracts high-level discriminative features from vibration signals and predicts equipment state. Against the limitations of traditional machine learning (ML)-based classifiers, the convolutional neural network (CNN) and deep neural network (DNN) are not only superior for real-time applications, but they also come with other benefits including ease-of-use, automated feature learning, and higher predictive accuracies. This study proposes a hybrid DNN and one-dimensional CNN diagnostics model ( D-dCNN ) which automatically extracts high-level discriminative features from vibration signals for FDI. Via Softmax averaging at the output layer, the model mitigates the limitations of the standalone classifiers. A diagnostic case study demonstrates the efficiency of the model with a significant accuracy of 92% (F1 score) and extensive comparative empirical validations.

Suggested Citation

  • Ugochukwu Ejike Akpudo & Jang-Wook Hur, 2021. "D-dCNN : A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics," Energies, MDPI, vol. 14(17), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5286-:d:622174
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/17/5286/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/17/5286/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Xiaolong Zhang & Xiaoguang Wei & Lin Zheng & Chenghao Wang & Huafeng Wang, 2022. "Research on Vibration Data-Driven Fault Diagnosis for Iron Core Looseness of Saturable Reactor in UHVDC Thyristor Valve Based on CVAE-GAN and Multimodal Feature Integrated CNN," Energies, MDPI, vol. 15(24), pages 1-24, 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:jeners:v:14:y:2021:i:17:p:5286-:d:622174. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.