IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i11p1724-d1663268.html
   My bibliography  Save this article

Review on Sound-Based Industrial Predictive Maintenance: From Feature Engineering to Deep Learning

Author

Listed:
  • Tongzhou Ye

    (The School of Mechatronics Engineering, The Anhui University of Science and Technology, Huainan 232001, China
    Department of Industrial and Systems Engineering, The Research Institute for Advanced Manufacturing, The Hong Kong Polytechnic University, Hong Kong 999077, China)

  • Tianhao Peng

    (The School of Mechatronics Engineering, The Anhui University of Science and Technology, Huainan 232001, China)

  • Lidong Yang

    (Department of Industrial and Systems Engineering, The Research Institute for Advanced Manufacturing, The Hong Kong Polytechnic University, Hong Kong 999077, China)

Abstract

Sound-based predictive maintenance (PdM) is a critical enabler for ensuring operational continuity and productivity in industrial systems. Due to the diversity of equipment types and the complexity of working environments, numerous feature engineering methods and anomaly diagnosis models have been developed based on sound signals. However, existing reviews focus more on the structures and results of the detection model, while neglecting the impact of the differences in feature engineering on subsequent detection models. Therefore, this paper aims to provide a comprehensive review of the state-of-the-art feature extraction methods based on sound signals. The judgment standards in the sound detection models are analyzed from empirical thresholding to machine learning and deep learning. The advantages and limitations of sound detection algorithms in varied equipment are elucidated through detailed examples and descriptions, providing a comprehensive understanding of performance and applicability. This review also provides a guide to the selection of feature extraction and detection methods for the predictive maintenance of equipment based on sound signals.

Suggested Citation

  • Tongzhou Ye & Tianhao Peng & Lidong Yang, 2025. "Review on Sound-Based Industrial Predictive Maintenance: From Feature Engineering to Deep Learning," Mathematics, MDPI, vol. 13(11), pages 1-31, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1724-:d:1663268
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/11/1724/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/11/1724/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu Chen & Rui Chang & Jifeng Guo, 2021. "Emotion Recognition of EEG Signals Based on the Ensemble Learning Method: AdaBoost," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, January.
    2. Kang Bai & Yong Zhou & Zhibo Cui & Weiwei Bao & Nan Zhang & Yongjie Zhai, 2022. "HOG-SVM-Based Image Feature Classification Method for Sound Recognition of Power Equipments," Energies, MDPI, vol. 15(12), pages 1-12, June.
    3. Mario A. Saucedo-Espinosa & Hugo Jair Escalante & Arturo Berrones, 2017. "Detection of defective embedded bearings by sound analysis: a machine learning approach," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 489-500, February.
    Full references (including those not matched with items on IDEAS)

    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. Tian Wang & Meina Qiao & Mengyi Zhang & Yi Yang & Hichem Snoussi, 2020. "Data-driven prognostic method based on self-supervised learning approaches for fault detection," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1611-1619, October.

    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:13:y:2025:i:11:p:1724-:d:1663268. 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.