IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v11y2025i2id1371.html

Leveraging AI in the Manufacturing Industry for Predictive Maintenance: A Technical Perspective

Author

Listed:
  • Alok Singh

Abstract

This technical article explores the transformative role of artificial intelligence in revolutionizing predictive maintenance practices across the manufacturing industry. By examining the integration of Internet of Things sensor networks with sophisticated machine learning algorithms, the article demonstrates how manufacturers can transition from traditional reactive maintenance approaches to proactive failure prediction systems. The discussion encompasses the technical infrastructure required for implementation, analytical models that drive accurate predictions, integration challenges with existing manufacturing systems, and methods for quantifying return on investment. Through examination of real-world applications and emerging technologies, the article provides a comprehensive framework for understanding how AI-enabled predictive maintenance solutions enhance operational efficiency, extend equipment lifespan, and deliver significant maintenance cost reductions in industrial environments.

Suggested Citation

  • Alok Singh, 2025. "Leveraging AI in the Manufacturing Industry for Predictive Maintenance: A Technical Perspective," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(2), pages 3258-3267, March.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1371
    DOI: 10.32628/CSEIT25112804
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112804
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT25112804
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/home/article/download/CSEIT25112804/CSEIT25112804
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/CSEIT25112804?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jbh:ijsrcs:v11:y2025:i2:id:1371. 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: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    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.