BEARING FAULT DETECTION ON WIND TURBINE GEARBOX VIBRATIONS USING GENERALIZED LIKELIHOOD RATIO-BASED INDICATORS

Kayacan Kestel, Cédric Peeters, Jérôme Antoni, Shawn Sheng, Jan Helsen

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

Abstract

Studies in condition monitoring literature often aim to detect rolling element bearing faults because they have one of the biggest shares among defects in turbo machinery. Accordingly, several prognosis and diagnosis methods have been devised to identify fault signatures from vibration signals. A recently proposed method to capture the rolling element bearing degradation provides the groundwork for new indicator families utilizing the generalized likelihood ratio test. This novel approach exploits the cyclostationarity and the impulsiveness of vibration signals independently in order to estimate the most suitable indicators for a given fault. However, the method has yet to be tested on complex experimental vibration signals such as those of a wind turbine gearbox. In this study, the approach is applied to the National Renewable Energy Laboratory Wind Turbine Gearbox Condition Monitoring Round Robin Study data set for bearing fault detection purposes. The data set is measured on an experimental test rig of a wind turbine gearbox; hence the complexity of the vibration signals is similar to a real case. The outcome demonstrates that the proposed method is capable of distinguishing between healthy and damaged vibration signals measured on a complex wind turbine gearbox.
Original languageEnglish
Title of host publicationProceedings of the ASME 2022 Turbomachinery Technical Conference & Exposition
PublisherAmerican Society of Mechanical Engineers(ASME)
Pages1-12
Number of pages12
Volume11
DOIs
Publication statusPublished - 2022
EventTurbo Expo 2022 Turbomachinery Technical Conference & Exposition: GT2022 - Rotterdam Ahoy Convention Centre, Rotterdam, Netherlands
Duration: 13 Jun 202217 Jun 2022
https://event.asme.org/Turbo-Expo-2022

Conference

ConferenceTurbo Expo 2022 Turbomachinery Technical Conference & Exposition
CountryNetherlands
CityRotterdam
Period13/06/2217/06/22
Internet address

Bibliographical note

Funding Information:
Jan Helsen received funding from the Flemish Government (AI Research Program). The authors would also like to acknowledge FWO (Fonds Wetenschappelijk Onderzoek) for their support through the post-doctoral grant of Cédric Peeters (#1282221N). They would also like to acknowledge FWO for the support through the SBO Robustify project (S006119N). The authors would also like to acknowledge the support of De Blauwe Cluster through the project Supersized 4.0 and the agency for Innovation by Science and Technology in Belgium for supporting the SIM MaSiWEC project. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

Funding Information:
Jan Helsen received funding from the Flemish Government (AI Research Program). The authors would also like to acknowledge FWO (Fonds Wetenschappelijk Onderzoek) for their support through the post-doctoral grant of Cédric Peeters (#1282221N). They would also like to acknowledge FWO for the support through the SBO Robustify project (S006119N). The authors would also like to acknowledge the support of De Blauwe Cluster through the project Supersized 4.0 and the agency for Innovation by Science and Technology in Belgium for supporting the SIM MaSiWEC project.

Funding Information:
This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

Publisher Copyright:
Copyright © 2022 by ASME and National Renewable Energy Lab (NREL)

Copyright:
Copyright 2022 Elsevier B.V., All rights reserved.

Keywords

  • fault detection
  • likelihood ratio test
  • cyclostationary
  • impulsiveness
  • condition monitoring

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