Institut for Forretningsudvikling og Teknologi

Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications - The State-of-the-Art Approaches

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

DOI

  • K. Mohana Sundaram, Anna University
  • ,
  • Azham Hussain, University Utara Malaysia
  • ,
  • P. Sanjeevikumar
  • Jens Bo Holm-Nielsen, Aalborg Universitet
  • ,
  • Vishnu Kumar Kaliappan, Anna University
  • ,
  • B. Kavya Santhoshi, Anna University

Deep learning (DL) is an exciting field of interest for many researchers and business. Due to a massive leap in DL based research, many domains like Business, science and government sectors make use of DL for various applications. This work puts forward the importance of DL and its application in a few critical electrical segments. Initially, an introduction to Artificial Intelligence (AI) and Machine Learning (ML) is presented. Then the need for DL and the popular architectures, algorithms and frameworks used are presented. A summary of different techniques used in DL is outlined, and finally, a review on the application of deep learning techniques in some popular electrical applications is presented. Five critical electrical applications, namely identification of bearing faults, hot spots on the surface of PV panels, insulator faults, an inspection of power lines and Electric vehicles have been considered for review in this work. The primary aim of this work is to present chronologically, a survey of different areas in which it applies DL along with their architectures, frameworks and techniques to provide a deeper understanding of DL for widespread use in real-time applications.

OriginalsprogEngelsk
TidsskriftIEEE Access
Vol/bind9
Sider (fra-til)41246-41260
Antal sider15
ISSN2169-3536
DOI
StatusUdgivet - feb. 2021

Bibliografisk note

Publisher Copyright:
© 2013 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Se relationer på Aarhus Universitet Citationsformater

ID: 218735922