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Vibration Fault Diagnosis in Wind Turbines Based on Automated Feature Learning

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  • Angela Meyer

    (School of Engineering and Computer Science, Bern University of Applied Sciences, 2501 Biel, Switzerland)

Abstract

A growing number of wind turbines are equipped with vibration measurement systems to enable the close monitoring and early detection of developing fault conditions. The vibration measurements are analyzed to continuously assess the component health and prevent failures that can result in downtimes. This study focuses on gearbox monitoring but is also applicable to other subsystems. The current state-of-the-art gearbox fault diagnosis algorithms rely on statistical or machine learning methods based on fault signatures that have been defined by human analysts. This has multiple disadvantages. Defining the fault signatures by human analysts is a time-intensive process that requires highly detailed knowledge of gearbox composition. This effort needs to be repeated for every new turbine, so it does not scale well with the increasing number of monitored turbines, especially in fast-growing portfolios. Moreover, fault signatures defined by human analysts can result in biased and imprecise decision boundaries that lead to imprecise and uncertain fault diagnosis decisions. We present a novel accurate fault diagnosis method for vibration-monitored wind turbine components that overcomes these disadvantages. Our approach combines autonomous data-driven learning of fault signatures and health state classification based on convolutional neural networks and isolation forests. We demonstrate its performance with vibration measurements from two wind turbine gearboxes. Unlike the state-of-the-art methods, our approach does not require gearbox-type-specific diagnosis expertise and is not restricted to predefined frequencies or spectral ranges but can monitor the full spectrum at once.

Suggested Citation

  • Angela Meyer, 2022. "Vibration Fault Diagnosis in Wind Turbines Based on Automated Feature Learning," Energies, MDPI, vol. 15(4), pages 1-13, February.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1514-:d:752365
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    Citations

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    Cited by:

    1. David Pérez Granados & Mauricio Alberto Ortega Ruiz & Joel Moreira Acosta & Sergio Arturo Gama Lara & Roberto Adrián González Domínguez & Pedro Jacinto Páramo Kañetas, 2023. "A Wind Turbine Vibration Monitoring System for Predictive Maintenance Based on Machine Learning Methods Developed under Safely Controlled Laboratory Conditions," Energies, MDPI, vol. 16(5), pages 1-17, February.
    2. Yanfeng He & Zhijie Guo & Xiang Wang & Waheed Abdul, 2023. "A Hybrid Approach of the Deep Learning Method and Rule-Based Method for Fault Diagnosis of Sucker Rod Pumping Wells," Energies, MDPI, vol. 16(7), pages 1-19, March.
    3. Stefan Jonas & Dimitrios Anagnostos & Bernhard Brodbeck & Angela Meyer, 2023. "Vibration Fault Detection in Wind Turbines Based on Normal Behaviour Models without Feature Engineering," Energies, MDPI, vol. 16(4), pages 1-16, February.
    4. Idris Issaadi & Kamel Eddine Hemsas & Abdenour Soualhi, 2023. "Wind Turbine Gearbox Diagnosis Based on Stator Current," Energies, MDPI, vol. 16(14), pages 1-19, July.

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