IDEAS home Printed from https://ideas.repec.org/h/ito/pchaps/211320.html
   My bibliography  Save this book chapter

Machine Health Monitoring and Fault Diagnosis Techniques Review in Industrial Power-Line Network

In: Modeling and Simulation in Engineering - Selected Problems

Author

Listed:
  • Saud Altaf
  • Shafiq Ahmad

Abstract

The machinery arrangements in industrial environment normally consist of motors of diverse sizes and specifications that are provided power and connected with common power-bus. The power-line could be act as a good source for travelling the signal through power-line network and this can be leave a faulty symptom while inspection of motors. This influence on other neighbouring motors with noisy signal that may present some type of fault condition in healthy motors. Further intricacy arises when this type of signal is propagated on power-line network by motors at different slip speeds, power rating and many faulty motors within the network. This sort of convolution and diversification of signals from multiple motors makes it challenging to measure and accurately relate to a certain motor or specific fault. This chapter presents a critical literature review analysis on machine-fault diagnosis and its related topics. The review covers a wide range of recent literature in this problem domain. A significant related research development and contribution of different areas regarding fault diagnosis and traceability within power-line networks will be discussed in detail throughout this chapter.

Suggested Citation

  • Saud Altaf & Shafiq Ahmad, 2020. "Machine Health Monitoring and Fault Diagnosis Techniques Review in Industrial Power-Line Network," Chapters, in: Jan Valdman & Leszek Marcinkowski (ed.), Modeling and Simulation in Engineering - Selected Problems, IntechOpen.
  • Handle: RePEc:ito:pchaps:211320
    DOI: 10.5772/intechopen.92044
    as

    Download full text from publisher

    File URL: https://www.intechopen.com/chapters/71801
    Download Restriction: no

    File URL: https://libkey.io/10.5772/intechopen.92044?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

    fault diagnosis; machine health monitoring; signal processing; industrial power-line network; artificial intelligence; wireless sensor network;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

    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:ito:pchaps:211320. 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: Slobodan Momcilovic (email available below). General contact details of provider: http://www.intechopen.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.