Enhanced demodulation band selection based on Operational Modal Analysis (OMA) for bearing diagnostics

Jesper Berntsen; Anders Brandt; Konstantinos Gryllias

doi:10.1016/j.ymssp.2022.109300

Enhanced demodulation band selection based on Operational Modal Analysis (OMA) for bearing diagnostics

Jesper Berntsen^*
, Anders Brandt
, Konstantinos Gryllias

^*Kontaktforfatter

Institut for Mekanik og Elektronik (IME)

KU Leuven
Flanders Make
Lindø Offshore Renewables Center
Aarhus Universitet

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

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Abstract

Vibration time signals emitted by a defective rolling element bearing are often used to determine its health condition. For real-world measurements these fault signals may be weak compared to the remaining content in the measured signal causing increased difficulties to fault detection. One way of enhancing the fault detection is by demodulation at a filtered frequency band in which the Signal-to-Noise-Ratio (SNR) of the fault signal is high. In the present paper, we propose a method that uses the resonances obtained from Operational Modal Analysis (OMA) as center frequencies for the filter bands as it is assumed that the characteristic bearing fault frequencies modulate resonances or carrier frequencies close to them. An indicator is used to determine which of the resonances from the OMA enhance the fault signal the most and this selection is dependent on the bandwidth as well. The presented method is applied on signals from two experimental cases, an experimental test rig of a planetary gearbox and a wind turbine gearbox, and is compared to the Fast Kurtogram and Log-Cycligram to assess its performance for band selection. The results show that the presented method determines a suited band for band pass filtering which enhances the fault detection at the two complex use cases.

Originalsprog	Engelsk
Artikelnummer	109300
Tidsskrift	Mechanical Systems and Signal Processing
Vol/bind	181
Antal sider	17
ISSN	0888-3270
DOI	https://doi.org/10.1016/j.ymssp.2022.109300
Status	Udgivet - 1. dec. 2022

Bibliografisk note

Funding Information:
This work was supported by the Innovation Fund Denmark . The authors would like to thank Prof. Robert Randall and his research group at the University of New South Wales for providing the data set from their test rig. The authors would also like to thank the National Renewable Energy Laboratory, US for organizing the test on condition monitoring of the wind turbine gearbox and particularly Dr. S. Sheng providing the resulting data sets. That work was conducted [in part] by the National Renewable Energy Laboratory, US , operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE AC36-08GO28308 .

Publisher Copyright:
© 2022 The Author(s)

Finansiering

This work was supported by the Innovation Fund Denmark . The authors would like to thank Prof. Robert Randall and his research group at the University of New South Wales for providing the data set from their test rig. The authors would also like to thank the National Renewable Energy Laboratory, US for organizing the test on condition monitoring of the wind turbine gearbox and particularly Dr. S. Sheng providing the resulting data sets. That work was conducted [in part] by the National Renewable Energy Laboratory, US , operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE AC36-08GO28308 .

Dokumenter og links

10.1016/j.ymssp.2022.109300Licens: CC BY

Open Access VersionForlagets udgivne version, 10,1 MBLicens: CC BY

SDU link

Tjek adgangen til materialet i Syddansk Universitetsbiblioteks søgemaskine

Fault Detection of Rolling Element Bearings in a Full Scale Wind Turbine Test Bench
Berntsen, J., 26. aug. 2022, Syddansk Universitet. Det Tekniske Fakultet. 124 s.
Publikation: Afhandling › Ph.d.-afhandling

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Citationsformater

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title = "Enhanced demodulation band selection based on Operational Modal Analysis (OMA) for bearing diagnostics",

abstract = "Vibration time signals emitted by a defective rolling element bearing are often used to determine its health condition. For real-world measurements these fault signals may be weak compared to the remaining content in the measured signal causing increased difficulties to fault detection. One way of enhancing the fault detection is by demodulation at a filtered frequency band in which the Signal-to-Noise-Ratio (SNR) of the fault signal is high. In the present paper, we propose a method that uses the resonances obtained from Operational Modal Analysis (OMA) as center frequencies for the filter bands as it is assumed that the characteristic bearing fault frequencies modulate resonances or carrier frequencies close to them. An indicator is used to determine which of the resonances from the OMA enhance the fault signal the most and this selection is dependent on the bandwidth as well. The presented method is applied on signals from two experimental cases, an experimental test rig of a planetary gearbox and a wind turbine gearbox, and is compared to the Fast Kurtogram and Log-Cycligram to assess its performance for band selection. The results show that the presented method determines a suited band for band pass filtering which enhances the fault detection at the two complex use cases.",

keywords = "Bearing fault detection, Demodulation band selection, Deterministic/random separation, Envelope analysis, Operational modal analysis, Vibration-based condition monitoring",

author = "Jesper Berntsen and Anders Brandt and Konstantinos Gryllias",

note = "Funding Information: This work was supported by the Innovation Fund Denmark . The authors would like to thank Prof. Robert Randall and his research group at the University of New South Wales for providing the data set from their test rig. The authors would also like to thank the National Renewable Energy Laboratory, US for organizing the test on condition monitoring of the wind turbine gearbox and particularly Dr. S. Sheng providing the resulting data sets. That work was conducted [in part] by the National Renewable Energy Laboratory, US , operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE AC36-08GO28308 . Publisher Copyright: {\textcopyright} 2022 The Author(s)",

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AU - Berntsen, Jesper

AU - Brandt, Anders

AU - Gryllias, Konstantinos

N1 - Funding Information: This work was supported by the Innovation Fund Denmark . The authors would like to thank Prof. Robert Randall and his research group at the University of New South Wales for providing the data set from their test rig. The authors would also like to thank the National Renewable Energy Laboratory, US for organizing the test on condition monitoring of the wind turbine gearbox and particularly Dr. S. Sheng providing the resulting data sets. That work was conducted [in part] by the National Renewable Energy Laboratory, US , operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE AC36-08GO28308 . Publisher Copyright: © 2022 The Author(s)

PY - 2022/12/1

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N2 - Vibration time signals emitted by a defective rolling element bearing are often used to determine its health condition. For real-world measurements these fault signals may be weak compared to the remaining content in the measured signal causing increased difficulties to fault detection. One way of enhancing the fault detection is by demodulation at a filtered frequency band in which the Signal-to-Noise-Ratio (SNR) of the fault signal is high. In the present paper, we propose a method that uses the resonances obtained from Operational Modal Analysis (OMA) as center frequencies for the filter bands as it is assumed that the characteristic bearing fault frequencies modulate resonances or carrier frequencies close to them. An indicator is used to determine which of the resonances from the OMA enhance the fault signal the most and this selection is dependent on the bandwidth as well. The presented method is applied on signals from two experimental cases, an experimental test rig of a planetary gearbox and a wind turbine gearbox, and is compared to the Fast Kurtogram and Log-Cycligram to assess its performance for band selection. The results show that the presented method determines a suited band for band pass filtering which enhances the fault detection at the two complex use cases.

AB - Vibration time signals emitted by a defective rolling element bearing are often used to determine its health condition. For real-world measurements these fault signals may be weak compared to the remaining content in the measured signal causing increased difficulties to fault detection. One way of enhancing the fault detection is by demodulation at a filtered frequency band in which the Signal-to-Noise-Ratio (SNR) of the fault signal is high. In the present paper, we propose a method that uses the resonances obtained from Operational Modal Analysis (OMA) as center frequencies for the filter bands as it is assumed that the characteristic bearing fault frequencies modulate resonances or carrier frequencies close to them. An indicator is used to determine which of the resonances from the OMA enhance the fault signal the most and this selection is dependent on the bandwidth as well. The presented method is applied on signals from two experimental cases, an experimental test rig of a planetary gearbox and a wind turbine gearbox, and is compared to the Fast Kurtogram and Log-Cycligram to assess its performance for band selection. The results show that the presented method determines a suited band for band pass filtering which enhances the fault detection at the two complex use cases.

KW - Bearing fault detection

KW - Demodulation band selection

KW - Deterministic/random separation

KW - Envelope analysis

KW - Operational modal analysis

KW - Vibration-based condition monitoring

U2 - 10.1016/j.ymssp.2022.109300

DO - 10.1016/j.ymssp.2022.109300

M3 - Journal article

AN - SCOPUS:85133342878

SN - 0888-3270

VL - 181

JO - Mechanical Systems and Signal Processing

JF - Mechanical Systems and Signal Processing

M1 - 109300

ER -

Enhanced demodulation band selection based on Operational Modal Analysis (OMA) for bearing diagnostics

Abstract

Bibliografisk note

Finansiering

Dokumenter og links

SDU link

Fingeraftryk

Relaterede publikationer

Fault Detection of Rolling Element Bearings in a Full Scale Wind Turbine Test Bench

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