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
Neural Computing and Applications
Published in: Neural Computing and Applications 4/2010

01-06-2010 | Original Article

Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor

Authors: Jaroslaw Kurek, Stanislaw Osowski

Published in: Neural Computing and Applications | Issue 4/2010

Log in

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

search-config
loading …

Abstract

The paper presents an automatic computerized system for the diagnosis of the rotor bars of the induction electrical motor by applying the support vector machine. Two solutions of diagnostic system have been elaborated. The first one, called fault detection, discovers only the case of the fault occurrence. The second one (complex diagnosis) is able to find which bars have been damaged. The most important problem is concerned with the generation and selection of the diagnostic features, on the basis of which the recognition of the state of the rotor bars is done. In our approach, we use the spectral information of the motor current, voltage and shaft field of one phase registered in an instantaneous form. The selected features form the input vector applied to the support vector machine, used as the classifier. The results of the numerical experiments are presented and discussed in the paper.
previous article A general graph-based semi-supervised learning with novel class discovery
next article Limit cycle prediction of a neurocontrol vehicle system based on gain-phase margin analysis

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

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+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
Literature
1.
Aydin I, Karaköse M, Akin E (2007) Artificial immune based support vector machine algorithm for fault diagnosis of induction motors. IEEE Electr Mach Syst Conf (ICEMS) 2:1–5
2.
Duda RO, Hart PE, Stork P (2003) Pattern classification and scene analysis. Wiley, New York
3.
Fang R (2006) Induction machine rotor diagnosis using support vector machines and rough set. Lect Notes Comput Sci 4114:631–636CrossRef
4.
Fenger M, Susnik M, Laderoute P, Thomson W (2003) Development of a tool to detect faults in induction motors via current signature analysis. Iris Rot Mach Conf, Santa Monica, pp 1–8
5.
Filippetti F, Francheschini G, Tassoni C, Vas P (2000) Recent developments of induction motor drives fault diagnosis using AI techniques. IEEE Trans Ind Electron 47:994–1004CrossRef
6.
Haji M, Toliyat H (2001) Pattern recognition—a technique for induction machine rotor fault detection “Broken bar fault”. IEEE IAS Trans 37:899–904
7.
Hubert C (2002) Electric machines: theory, operating applications, and controls. Prentice Hall, Englewood Cliffs
8.
Kliman GB, Stein J (1990) Induction motor fault detection via passive current monitoring. In: Proceedings of international conference (ICEM’90), MIT, Boston, USA, pp 13–17
9.
Li L, Mechefske CK, Li W (2004) Electric motor faults diagnosis using artificial neural networks. Insight—Non-Destruct Test Cond Monit 46:616–621CrossRef
10.
Kurek J, Biernat A, Osowski S, Markiewicz T (2006) Diagnosis of induction motor using support vector machine, VII Conference CPEE, Odessa, Ukraine, 98–101
11.
Matlab user manual (2002) MathWorks, Natick, USA
12.
Meyer D, Leisch F, Hornik K (2003) The support vector machine under test. Neurocomputing 55:169–186CrossRef
13.
Mirafzal B, Demerdash N (2004) Induction machine broken-bar fault diagnosis using the rotor magnetic field space vector orientation. IEEE Trans Ind Appl 40:534–542CrossRef
14.
Penman J, Sedding HG, Lloyd BA, Fink WT (1994) Detection and location of inertium short circuits in the stator winding of operating motors. IEEE Trans Energy Conserv 9:694–699
15.
Schölkopf B, Smola A (2002) Learning with kernels. MIT Press, Cambridge
16.
Schoen R, Lin BK, Habetler FG, Schlog HJ, Farag S (1995) An unsupervised on-line system for induction motor fault detection using stator current monitoring. IEEE IAS Trans 31:1280–1286
17.
Su H, Chong KT, Parlos AG (2005) A neural network method for induction machine fault detection with vibration signal. Lect Notes Comput Sci Springer, Berlin, vol 3481, pp 1293–1302
18.
Supangat R, Ertugrul N, Soong WL, Gray DA, Hansen CH, Grieger J (2005) Broken rotor bar fault detection in induction motors using starting current analysis. In: Proceedings of 11th European conference on power electronics and applications, Dresden, Germany, pp 1–10
19.
Szabo L, Dobai JB, Biro KA (2004) Rotor faults detection in squirrel-cage induction motors by current signature analysis, 2004 IEEE-TTTC—international conference on automation, quality and testing, robotics, Cluj-Napoca, Romania, pp 1–4
20.
Metadata
Title
Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor
Authors
Jaroslaw Kurek
Stanislaw Osowski
Publication date
01-06-2010
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 4/2010
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-009-0316-5

Other articles of this Issue 4/2010

Neural Computing and Applications 4/2010 Go to the issue

Original Article

A comparative study on authorship attribution classification tasks using both neural network and statistical methods

Original Article

Neural computing with genetic algorithm in evaluating potentially hazardous metropolitan areas result from earthquake

Call for Papers

Special Issue on Artificial Immune Systems: Theory and Applications

Original Article

Chaotic neural network controlled by particle swarm with decaying chaotic inertia weight for pattern recognition

Original Article

Generalized fuzzy interior ideals of semigroups

Original Article

A comprehensive evaluation of multicategory classification methods for fault classification in series compensated transmission line

Premium Partner

    Image Credits