Empirical mode decomposition of motor current signatures for centrifugal pump diagnostics

Samir Alabied, Usama Haba, Alsadak Daraz, Fengshou Gu, Andrew D. Ball

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

Abstract

Motor current signature analysis (MCSA) is an important, reliable and non-invasive technique for monitoring rotation machines. Spectrum analysis is a common way to implement MCSA, which allows large faults such as severe mechanical imbalance to be extracted successfully, but is often ineffective in the detection of incipient faults such as supporting bearings from motor drive systems because of noise and nonlinear interferences. To improve the performance of MSCA, this paper exploits the use of Empirical Mode Decomposition (EMD) method as an advanced tool to process motor current signals for noise reduction and nonlinear signature enhancement. The nonlinear demodulation property of EMD is firstly reviewed in association with the motor current signal models with fault cases. Then EMD is applied to signals from different fault cases from a centrifuge pump system to verify its performances in extracting the fault signatures for separating different faults. In conjunction with the envelope spectrum of separated intrinsic mode function (IMF), it shows that the proposed EMD based approach produces a better result in diagnosing common pump faults: small defects on impeller and bearings, which cannot be separated based on spectrum analysis.

Original languageEnglish
Title of host publication2018 24th IEEE International Conference on Automation and Computing (ICAC)
Subtitle of host publicationImproving Productivity through Automation and Computing
EditorsXiandong Ma
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781862203426, 9781862203419
ISBN (Print)9781538648919
DOIs
Publication statusPublished - 1 Jul 2019
Event24th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing - Newcastle University, Newcastle upon Tyne, United Kingdom
Duration: 6 Sep 20187 Sep 2018
Conference number: 24
https://ieeexplore.ieee.org/xpl/conhome/8742895/proceeding (Website Containing the Proceedings)
http://www.cacsuk.co.uk/index.php/conferences/icac (Link to Conference Information)

Conference

Conference24th IEEE International Conference on Automation and Computing
Abbreviated titleICAC 2018
CountryUnited Kingdom
CityNewcastle upon Tyne
Period6/09/187/09/18
Internet address

Fingerprint

Centrifugal Pump
Centrifugal pumps
Diagnostics
Fault
Signature
Decomposition
Decompose
Bearings (structural)
Spectrum analysis
Spectrum Analysis
Pumps
Pump
Centrifuges
Demodulation
Noise abatement
Intrinsic Mode Function
Noise Reduction
Association reactions
Decomposition Method
Envelope

Cite this

Alabied, S., Haba, U., Daraz, A., Gu, F., & Ball, A. D. (2019). Empirical mode decomposition of motor current signatures for centrifugal pump diagnostics. In X. Ma (Ed.), 2018 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing [8749109] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2018.8749109
Alabied, Samir ; Haba, Usama ; Daraz, Alsadak ; Gu, Fengshou ; Ball, Andrew D. / Empirical mode decomposition of motor current signatures for centrifugal pump diagnostics. 2018 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing. editor / Xiandong Ma. Institute of Electrical and Electronics Engineers Inc., 2019.
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abstract = "Motor current signature analysis (MCSA) is an important, reliable and non-invasive technique for monitoring rotation machines. Spectrum analysis is a common way to implement MCSA, which allows large faults such as severe mechanical imbalance to be extracted successfully, but is often ineffective in the detection of incipient faults such as supporting bearings from motor drive systems because of noise and nonlinear interferences. To improve the performance of MSCA, this paper exploits the use of Empirical Mode Decomposition (EMD) method as an advanced tool to process motor current signals for noise reduction and nonlinear signature enhancement. The nonlinear demodulation property of EMD is firstly reviewed in association with the motor current signal models with fault cases. Then EMD is applied to signals from different fault cases from a centrifuge pump system to verify its performances in extracting the fault signatures for separating different faults. In conjunction with the envelope spectrum of separated intrinsic mode function (IMF), it shows that the proposed EMD based approach produces a better result in diagnosing common pump faults: small defects on impeller and bearings, which cannot be separated based on spectrum analysis.",
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Alabied, S, Haba, U, Daraz, A, Gu, F & Ball, AD 2019, Empirical mode decomposition of motor current signatures for centrifugal pump diagnostics. in X Ma (ed.), 2018 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing., 8749109, Institute of Electrical and Electronics Engineers Inc., 24th IEEE International Conference on Automation and Computing, Newcastle upon Tyne, United Kingdom, 6/09/18. https://doi.org/10.23919/IConAC.2018.8749109

Empirical mode decomposition of motor current signatures for centrifugal pump diagnostics. / Alabied, Samir; Haba, Usama; Daraz, Alsadak; Gu, Fengshou; Ball, Andrew D.

2018 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing. ed. / Xiandong Ma. Institute of Electrical and Electronics Engineers Inc., 2019. 8749109.

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

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AU - Haba, Usama

AU - Daraz, Alsadak

AU - Gu, Fengshou

AU - Ball, Andrew D.

PY - 2019/7/1

Y1 - 2019/7/1

N2 - Motor current signature analysis (MCSA) is an important, reliable and non-invasive technique for monitoring rotation machines. Spectrum analysis is a common way to implement MCSA, which allows large faults such as severe mechanical imbalance to be extracted successfully, but is often ineffective in the detection of incipient faults such as supporting bearings from motor drive systems because of noise and nonlinear interferences. To improve the performance of MSCA, this paper exploits the use of Empirical Mode Decomposition (EMD) method as an advanced tool to process motor current signals for noise reduction and nonlinear signature enhancement. The nonlinear demodulation property of EMD is firstly reviewed in association with the motor current signal models with fault cases. Then EMD is applied to signals from different fault cases from a centrifuge pump system to verify its performances in extracting the fault signatures for separating different faults. In conjunction with the envelope spectrum of separated intrinsic mode function (IMF), it shows that the proposed EMD based approach produces a better result in diagnosing common pump faults: small defects on impeller and bearings, which cannot be separated based on spectrum analysis.

AB - Motor current signature analysis (MCSA) is an important, reliable and non-invasive technique for monitoring rotation machines. Spectrum analysis is a common way to implement MCSA, which allows large faults such as severe mechanical imbalance to be extracted successfully, but is often ineffective in the detection of incipient faults such as supporting bearings from motor drive systems because of noise and nonlinear interferences. To improve the performance of MSCA, this paper exploits the use of Empirical Mode Decomposition (EMD) method as an advanced tool to process motor current signals for noise reduction and nonlinear signature enhancement. The nonlinear demodulation property of EMD is firstly reviewed in association with the motor current signal models with fault cases. Then EMD is applied to signals from different fault cases from a centrifuge pump system to verify its performances in extracting the fault signatures for separating different faults. In conjunction with the envelope spectrum of separated intrinsic mode function (IMF), it shows that the proposed EMD based approach produces a better result in diagnosing common pump faults: small defects on impeller and bearings, which cannot be separated based on spectrum analysis.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Alabied S, Haba U, Daraz A, Gu F, Ball AD. Empirical mode decomposition of motor current signatures for centrifugal pump diagnostics. In Ma X, editor, 2018 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing. Institute of Electrical and Electronics Engineers Inc. 2019. 8749109 https://doi.org/10.23919/IConAC.2018.8749109