Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Fault Diagnosis in Direct Current Electric Motors via an Artificial Neural Network

Authors : Theofanis I. Aravanis, Tryfon-Chrysovalantis I. Aravanis, Polydoros N. Papadopoulos

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The combined problem of fault detection and classification (referred to as fault diagnosis) of Direct Current (DC) electric motors via a simple, yet powerful, technique based on an Artificial Neural Network (ANN) is proposed. The ability of an ANN in identifying patterns with high fidelity—without the need of any rigorous mathematical model of the system under investigation—leads to an excellent diagnosis performance, even for faults that result in almost indistinguishable output system responses (both in time and in frequency domain). The flexibility and speed of the presented method indicate that it can easily be applied to on-line fault diagnosis as well.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Cyber Security Incident Handling, Warning and Response System for the European Critical Information Infrastructures (CyberSANE)
next chapter On Predicting Bottlenecks in Wavefront Parallel Video Coding Using Deep Neural Networks
Footnotes
1
Artificial Neural Networks have been successively used on a variety of applications, including computer vision, speech recognition, machine translation and many more.
 
2
The model of the DC motor is exclusively used for the data generation of the healthy and faulty system.
 
3
Brush faults are quite common failures in DC motors, and they are typically a consequence of unsmooth contact of the brushes, dirty collector or brushes, unadjusted brush pressure springs, oval shaped collector and consumed brush life [1].
 
4
More evidently for the healthy case and the faulty scenario where \(L_{a}\) is increased by 200%.
 
5
TensorFlow was developed by the Google Brain team, and can be found at https://​www.​tensorflow.​org.
 
6
Over-fitting is the production of an analysis that corresponds too closely or exactly to a particular set of data, and may, therefore, fail to fit additional data or predict future observations reliably.
 
Literature
1.
Bay, O.F., Bayir, R.: A fault diagnosis of engine starting system via starter motors using fuzzy logic algorithm. Gazi Univ. J. Sci. 24, 437–449 (2011)
2.
Filbert, D., Schneider, C., Spannhake, S.: Model equation and fault detection of electric motors. Technical report, IFAC Fault Detection, Supervision and Safety for Technical Processes, Baden-Baden, Germany (1991)
3.
4.
Liu, X.Q., Zhang, H.Y., Liu, J., Yang, J.: Fault detection and diagnosis of permanent-magnet DC motor based on parameter estimation and neural network. IEEE Trans. Ind. Electron. 89, 1021–1030 (2000)
5.
Patan, K., Korbicz, J., Głowacki, G.: DC motor fault diagnosis by means of artificial neural network. In: Proceedings of the International Conference on Informatics in Control, Automation and Robotics, ICINCO 2007, pp. 11–18 (2007)
6.
Samarasinghe, S.: Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition. Auerbach Publications, Boca Raton (2006)CrossRef
7.
Trigeassou, J.C.: Electrical Machines Diagnosis. Wiley, Hoboken (2013)
8.
Yu, K., Yang, F., Guo, H., Xu, J.: Fault diagnosis and location of brushless DC motor system based on wavelet transform and artificial neural network. In: Proceedings of the 2010 International Conference on Electrical Machines and Systems. IEEE (2010)
Metadata
Title
Fault Diagnosis in Direct Current Electric Motors via an Artificial Neural Network
Authors
Theofanis I. Aravanis
Tryfon-Chrysovalantis I. Aravanis
Polydoros N. Papadopoulos
Copyright Year
2019
Book
Engineering Applications of Neural Networks

Print ISBN: 978-3-030-20256-9

Electronic ISBN: 978-3-030-20257-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-20257-6_42

Premium Partner

    Image Credits