IDEAS home Printed from https://ideas.repec.org/h/spr/ssrchp/978-3-030-36518-9_12.html
   My bibliography  Save this book chapter

Implementation of Predictive Maintenance Systems in Remotely Located Process Plants under Industry 4.0 Scenario

In: Advances in RAMS Engineering

Author

Listed:
  • P. G. Ramesh

    (The Assam Kaziranga University)

  • Saurav Jyoti Dutta

    (Numaligarh Refinery Ltd.)

  • Subhas Sarma Neog

    (Numaligarh Refinery Limited)

  • Prandip Baishya

    (Mechanical Maintenance Department and Area-in-Charge of Diesel Hydro-Treater Unit of Numaligarh Refinery Limited)

  • Indrani Bezbaruah

    (Kaziranga University)

Abstract

Rapid developments in technologies such as Robotics, Digital Automation, Internet of Things and AI have heralded the Fourth Industrial Revolution, commonly referred to as Industry 4.0 (i4.0). Industrial operations and products have since become more competitive and hence more demanding. Systems have also become more complex and inter-disciplinary in nature. Diligent surveillance of operating conditions of such systems and initiation of appropriate actions based on monitored conditions have become indispensable for sustainability of businesses. Significant amount of research is being undertaken world over to meet this requirement of the day. In line with the ongoing research, this paper highlights the need for identifying the needs of condition monitoring preparedness of process plants located in remote places, especially in a logistic sense. Issues related to assessment of the need for the new paradigm in condition monitoring, challenges faced by such plants in the transition from legacy systems to a new system and customisation and optimisation of Predictive Maintenance under Industry 4.0 (PdM 4.0) have been discussed. A Case Study pertaining to remote monitoring of a gas compressor system of a petroleum refinery in North Eastern India and a Case Discussion on Basic Technical Requirements for the implementation of Industrial internet of Things (IIOT) based predictive maintenance system are presented to highlight the benefits and issues associated with the radical shift in paradigm from legacy systems to Industry 4.0 based predictive maintenance (PdM 4.0) system. Frameworks for PdM 4.0 system decision making and development are also suggested for supporting future work in this area.

Suggested Citation

  • P. G. Ramesh & Saurav Jyoti Dutta & Subhas Sarma Neog & Prandip Baishya & Indrani Bezbaruah, 2020. "Implementation of Predictive Maintenance Systems in Remotely Located Process Plants under Industry 4.0 Scenario," Springer Series in Reliability Engineering, in: Durga Rao Karanki & Gopika Vinod & Srividya Ajit (ed.), Advances in RAMS Engineering, pages 293-326, Springer.
  • Handle: RePEc:spr:ssrchp:978-3-030-36518-9_12
    DOI: 10.1007/978-3-030-36518-9_12
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:ssrchp:978-3-030-36518-9_12. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.