Motor current signal analysis using a modified bispectrum for machine fault diagnosis

Fengshou Gu, Yimin Shao, Niaoqin Hu, Bruno Fazenda, Andrew Ball

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

9 Citations (Scopus)

Abstract

This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor. The theoretical basis is studied to understand current signal characteristics when the motor undertakes a varying load under faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is thus proposed to combine both lower sidebands and higher sidebands simultaneously and hence describe the current signal more accurately. Based on this new bispectrum a more effective diagnostic feature namely normalised bispectral peak is developed for fault classification. In association with the kurtosis of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from other fault cases and discharge valve leakage and intercooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment.

LanguageEnglish
Title of host publicationICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
Pages4890-4895
Number of pages6
Publication statusPublished - 2009
EventThe Society of Instrument and Control Engineers & The Institute of Control, Robotics and Systems International Joint Conference - International Congress Centre , Fukuoka, Japan
Duration: 18 Aug 200921 Aug 2009
http://www.sice.or.jp/org/sice2009/ (Link to Conference Website)

Conference

ConferenceThe Society of Instrument and Control Engineers & The Institute of Control, Robotics and Systems International Joint Conference
Abbreviated titleICROS-SICE 2009
CountryJapan
CityFukuoka
Period18/08/0921/08/09
Internet address

Fingerprint

Signal analysis
Failure analysis
Reciprocating compressors
Amplitude modulation
Induction motors
Stators
Classifiers

Cite this

Gu, F., Shao, Y., Hu, N., Fazenda, B., & Ball, A. (2009). Motor current signal analysis using a modified bispectrum for machine fault diagnosis. In ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings (pp. 4890-4895). [5334672]
Gu, Fengshou ; Shao, Yimin ; Hu, Niaoqin ; Fazenda, Bruno ; Ball, Andrew. / Motor current signal analysis using a modified bispectrum for machine fault diagnosis. ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. pp. 4890-4895
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abstract = "This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor. The theoretical basis is studied to understand current signal characteristics when the motor undertakes a varying load under faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is thus proposed to combine both lower sidebands and higher sidebands simultaneously and hence describe the current signal more accurately. Based on this new bispectrum a more effective diagnostic feature namely normalised bispectral peak is developed for fault classification. In association with the kurtosis of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from other fault cases and discharge valve leakage and intercooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment.",
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Gu, F, Shao, Y, Hu, N, Fazenda, B & Ball, A 2009, Motor current signal analysis using a modified bispectrum for machine fault diagnosis. in ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings., 5334672, pp. 4890-4895, The Society of Instrument and Control Engineers & The Institute of Control, Robotics and Systems International Joint Conference, Fukuoka, Japan, 18/08/09.

Motor current signal analysis using a modified bispectrum for machine fault diagnosis. / Gu, Fengshou; Shao, Yimin; Hu, Niaoqin; Fazenda, Bruno; Ball, Andrew.

ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. p. 4890-4895 5334672.

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

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N2 - This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor. The theoretical basis is studied to understand current signal characteristics when the motor undertakes a varying load under faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is thus proposed to combine both lower sidebands and higher sidebands simultaneously and hence describe the current signal more accurately. Based on this new bispectrum a more effective diagnostic feature namely normalised bispectral peak is developed for fault classification. In association with the kurtosis of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from other fault cases and discharge valve leakage and intercooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment.

AB - This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor. The theoretical basis is studied to understand current signal characteristics when the motor undertakes a varying load under faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is thus proposed to combine both lower sidebands and higher sidebands simultaneously and hence describe the current signal more accurately. Based on this new bispectrum a more effective diagnostic feature namely normalised bispectral peak is developed for fault classification. In association with the kurtosis of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from other fault cases and discharge valve leakage and intercooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment.

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Gu F, Shao Y, Hu N, Fazenda B, Ball A. Motor current signal analysis using a modified bispectrum for machine fault diagnosis. In ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. p. 4890-4895. 5334672