IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i24p4032-d1550191.html
   My bibliography  Save this article

Higher-Order Spectral Analysis and Artificial Intelligence for Diagnosing Faults in Electrical Machines: An Overview

Author

Listed:
  • Miguel Enrique Iglesias Martínez

    (Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Campus de Vera, Camino de Vera, s/n, Edificio 8E, Acceso F, 4ª Planta, 46022 Valencia, Spain)

  • Jose A. Antonino-Daviu

    (Instituto Tecnológico de la Energía, Parque Tecnológico de Valencia, Avenida Juan de la Cierva 24, 46980 Paterna, Spain)

  • Larisa Dunai

    (Centro de Investigación en Tecnologías Gráficas, Universitat Politècnica de València, Camino de Vera, s/n, Edificio 8H, 46022 Valencia, Spain)

  • J. Alberto Conejero

    (Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Campus de Vera, Camino de Vera, s/n, Edificio 8E, Acceso F, 4ª Planta, 46022 Valencia, Spain)

  • Pedro Fernández de Córdoba

    (Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Campus de Vera, Camino de Vera, s/n, Edificio 8E, Acceso F, 4ª Planta, 46022 Valencia, Spain)

Abstract

Fault diagnosis in electrical machines is a cornerstone of operational reliability and cost-effective maintenance strategies. This review provides a comprehensive exploration of the integration of higher-order spectral analysis (HOSA) techniques—such as a bispectrum, spectral kurtosis, and multifractal wavelet analysis—with advanced artificial intelligence (AI) methodologies, including deep learning, clustering algorithms, Transformer models, and transfer learning. The synergy between HOSA’s robustness in noisy and transient environments and AI’s automation of complex classifications has significantly advanced fault diagnosis in synchronous and DC motors. The novelty of this work lies in its detailed examination of the latest AI advancements, and the hybrid framework combining HOSA-derived features with AI techniques. The proposed approaches address challenges such as computational efficiency and scalability for industrial-scale applications, while offering innovative solutions for predictive maintenance. By leveraging these hybrid methodologies, the work charts a transformative path for improving the reliability and adaptability of industrial-grade electrical machine systems.

Suggested Citation

  • Miguel Enrique Iglesias Martínez & Jose A. Antonino-Daviu & Larisa Dunai & J. Alberto Conejero & Pedro Fernández de Córdoba, 2024. "Higher-Order Spectral Analysis and Artificial Intelligence for Diagnosing Faults in Electrical Machines: An Overview," Mathematics, MDPI, vol. 12(24), pages 1-23, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:24:p:4032-:d:1550191
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/24/4032/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/24/4032/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Patxi Gonzalez & Garikoitz Buigues & Angel Javier Mazon, 2023. "Noise in Electric Motors: A Comprehensive Review," Energies, MDPI, vol. 16(14), pages 1-22, July.
    2. Bilgin Umut Deveci & Mert Celtikoglu & Ozlem Albayrak & Perin Unal & Pinar Kirci, 2024. "Transfer Learning Enabled Bearing Fault Detection Methods Based on Image Representations of Single-Dimensional Signals," Information Systems Frontiers, Springer, vol. 26(4), pages 1345-1397, August.
    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. Moien Masoumi & Abeka Selliah & Berker Bilgin, 2024. "Development of an Experimental Acoustic Noise Characterization Setup for Electric Motor Drive Applications," Energies, MDPI, vol. 17(21), pages 1-21, October.
    2. Muhammad Younas & Irfan Awan, 2024. "Cloud, IoT and Data Science," Information Systems Frontiers, Springer, vol. 26(4), pages 1219-1222, August.

    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:12:y:2024:i:24:p:4032-:d:1550191. 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.