Modulation signal bispectrum analysis of motor current signals for stator fault diagnosis

A. Alwodai, X. Yuan, Y. Shao, F. Gu, A. D. Ball

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Induction motors are the most widely used electrical machines in industry. To diagnose any possible incipient faults, many techniques have been developed. Motor current signature analysis (MCSA) is a common practice in industry to find motor faults. However, because small modulations due to faults it is difficult to quantify it in the measured signals which predominates with supply frequency, higher order harmonics and noise. In this paper a modulation signal (MS) bispectrum is investigated to detect different severities of stator faults. It shows that MS bispectrum has the capability to accurately estimate modulation degrees and suppress the random and nonmodulation components. Test results show that MS bispectrum has a better performance in differentiating spectrum amplitudes due to stator faults and hence produces better diagnosis performance, compared with that of conventional power spectrum analysis.

LanguageEnglish
Title of host publicationICAC 12 - Proceedings of the 18th International Conference on Automation and Computing
Subtitle of host publicationIntegration of Design and Engineering
Pages96-101
Number of pages6
ISBN (Electronic)9781908549006
Publication statusPublished - 15 Oct 2012
Event18th International Conference on Automation and Computing: Integration of Design and Engineering - Loughborough University, Leicestershire, United Kingdom
Duration: 7 Sep 20128 Sep 2012
Conference number: 18
https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=20732 (Link to Conference Website)

Conference

Conference18th International Conference on Automation and Computing
Abbreviated titleICAC 2012
CountryUnited Kingdom
CityLeicestershire
Period7/09/128/09/12
Internet address

Fingerprint

Signal analysis
Stators
Failure analysis
Modulation
Power spectrum
Induction motors
Spectrum analysis
Industry

Cite this

Alwodai, A., Yuan, X., Shao, Y., Gu, F., & Ball, A. D. (2012). Modulation signal bispectrum analysis of motor current signals for stator fault diagnosis. In ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering (pp. 96-101). [6330495]
Alwodai, A. ; Yuan, X. ; Shao, Y. ; Gu, F. ; Ball, A. D. / Modulation signal bispectrum analysis of motor current signals for stator fault diagnosis. ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering. 2012. pp. 96-101
@inproceedings{95db824cf5204cca8b8a6b2262d4e805,
title = "Modulation signal bispectrum analysis of motor current signals for stator fault diagnosis",
abstract = "Induction motors are the most widely used electrical machines in industry. To diagnose any possible incipient faults, many techniques have been developed. Motor current signature analysis (MCSA) is a common practice in industry to find motor faults. However, because small modulations due to faults it is difficult to quantify it in the measured signals which predominates with supply frequency, higher order harmonics and noise. In this paper a modulation signal (MS) bispectrum is investigated to detect different severities of stator faults. It shows that MS bispectrum has the capability to accurately estimate modulation degrees and suppress the random and nonmodulation components. Test results show that MS bispectrum has a better performance in differentiating spectrum amplitudes due to stator faults and hence produces better diagnosis performance, compared with that of conventional power spectrum analysis.",
keywords = "bispectrum, Motor current signatur, power spectrum, stator fault",
author = "A. Alwodai and X. Yuan and Y. Shao and F. Gu and Ball, {A. D.}",
year = "2012",
month = "10",
day = "15",
language = "English",
isbn = "9781467317221",
pages = "96--101",
booktitle = "ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing",

}

Alwodai, A, Yuan, X, Shao, Y, Gu, F & Ball, AD 2012, Modulation signal bispectrum analysis of motor current signals for stator fault diagnosis. in ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering., 6330495, pp. 96-101, 18th International Conference on Automation and Computing, Leicestershire, United Kingdom, 7/09/12.

Modulation signal bispectrum analysis of motor current signals for stator fault diagnosis. / Alwodai, A.; Yuan, X.; Shao, Y.; Gu, F.; Ball, A. D.

ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering. 2012. p. 96-101 6330495.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Modulation signal bispectrum analysis of motor current signals for stator fault diagnosis

AU - Alwodai, A.

AU - Yuan, X.

AU - Shao, Y.

AU - Gu, F.

AU - Ball, A. D.

PY - 2012/10/15

Y1 - 2012/10/15

N2 - Induction motors are the most widely used electrical machines in industry. To diagnose any possible incipient faults, many techniques have been developed. Motor current signature analysis (MCSA) is a common practice in industry to find motor faults. However, because small modulations due to faults it is difficult to quantify it in the measured signals which predominates with supply frequency, higher order harmonics and noise. In this paper a modulation signal (MS) bispectrum is investigated to detect different severities of stator faults. It shows that MS bispectrum has the capability to accurately estimate modulation degrees and suppress the random and nonmodulation components. Test results show that MS bispectrum has a better performance in differentiating spectrum amplitudes due to stator faults and hence produces better diagnosis performance, compared with that of conventional power spectrum analysis.

AB - Induction motors are the most widely used electrical machines in industry. To diagnose any possible incipient faults, many techniques have been developed. Motor current signature analysis (MCSA) is a common practice in industry to find motor faults. However, because small modulations due to faults it is difficult to quantify it in the measured signals which predominates with supply frequency, higher order harmonics and noise. In this paper a modulation signal (MS) bispectrum is investigated to detect different severities of stator faults. It shows that MS bispectrum has the capability to accurately estimate modulation degrees and suppress the random and nonmodulation components. Test results show that MS bispectrum has a better performance in differentiating spectrum amplitudes due to stator faults and hence produces better diagnosis performance, compared with that of conventional power spectrum analysis.

KW - bispectrum

KW - Motor current signatur

KW - power spectrum

KW - stator fault

UR - http://www.scopus.com/inward/record.url?scp=84869454429&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781467317221

SP - 96

EP - 101

BT - ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing

ER -

Alwodai A, Yuan X, Shao Y, Gu F, Ball AD. Modulation signal bispectrum analysis of motor current signals for stator fault diagnosis. In ICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering. 2012. p. 96-101. 6330495