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Vibration Fault Detection in Wind Turbines Based on Normal Behaviour Models without Feature Engineering

Author

Listed:
  • Stefan Jonas

    (School of Engineering and Computer Science, Bern University of Applied Sciences, Quellgasse 21, 2501 Biel, Switzerland)

  • Dimitrios Anagnostos

    (WinJi AG, Badenerstrasse 808, 8048 Zurich, Switzerland)

  • Bernhard Brodbeck

    (WinJi AG, Badenerstrasse 808, 8048 Zurich, Switzerland)

  • Angela Meyer

    (School of Engineering and Computer Science, Bern University of Applied Sciences, Quellgasse 21, 2501 Biel, Switzerland)

Abstract

Most wind turbines are remotely monitored 24/7 to allow for early detection of operation problems and developing damage. We present a new fault detection approach for vibration-monitored drivetrains that does not require any feature engineering. Our method relies on a simple model architecture to enable a straightforward implementation in practice. We propose to apply convolutional autoencoders for identifying and extracting the most relevant features from a broad continuous range of the spectrum in an automated manner, saving time and effort. We focus on the range of [0, 1000] Hz for demonstration purposes. A spectral model of the normal vibration response is learnt for the monitored component from past measurements. We demonstrate that the trained model can successfully distinguish damaged from healthy components and detect a damaged generator bearing and damaged gearbox parts from their vibration responses. Using measurements from commercial wind turbines and a test rig, we show that vibration-based fault detection in wind turbine drivetrains can be performed without the usual upfront definition of spectral features. Another advantage of the presented method is that a broad continuous range of the spectrum can be monitored instead of the usual focus on monitoring individual frequencies and harmonics. Future research should investigate the proposed method on more comprehensive datasets and fault types.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1760-:d:1063979
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    References listed on IDEAS

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    1. Meyer, Angela, 2021. "Multi-target normal behaviour models for wind farm condition monitoring," Applied Energy, Elsevier, vol. 300(C).
    2. Angela Meyer, 2022. "Vibration Fault Diagnosis in Wind Turbines Based on Automated Feature Learning," Energies, MDPI, vol. 15(4), pages 1-13, February.
    3. Teng, Wei & Ding, Xian & Zhang, Xiaolong & Liu, Yibing & Ma, Zhiyong, 2016. "Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform," Renewable Energy, Elsevier, vol. 93(C), pages 591-598.
    4. Feng, Zhipeng & Qin, Sifeng & Liang, Ming, 2016. "Time–frequency analysis based on Vold-Kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions," Renewable Energy, Elsevier, vol. 85(C), pages 45-56.
    5. Teng, Wei & Ding, Xian & Cheng, Hao & Han, Chen & Liu, Yibing & Mu, Haihua, 2019. "Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform," Renewable Energy, Elsevier, vol. 136(C), pages 393-402.
    6. Li, Yanting & Jiang, Wenbo & Zhang, Guangyao & Shu, Lianjie, 2021. "Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data," Renewable Energy, Elsevier, vol. 171(C), pages 103-115.
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    Cited by:

    1. Rabie Aloui & Raoudha Gaha & Barbara Lafarge & Berk Celik & Caroline Verdari, 2024. "Life Cycle Assessment of Piezoelectric Devices Implemented in Wind Turbine Condition Monitoring Systems," Energies, MDPI, vol. 17(16), pages 1-20, August.
    2. K. Ramakrishna Kini & Fouzi Harrou & Muddu Madakyaru & Ying Sun, 2023. "Enhancing Wind Turbine Performance: Statistical Detection of Sensor Faults Based on Improved Dynamic Independent Component Analysis," Energies, MDPI, vol. 16(15), pages 1-25, August.
    3. Lorin Jenkel & Stefan Jonas & Angela Meyer, 2023. "Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning," Energies, MDPI, vol. 16(17), pages 1-29, September.
    4. Piotr Sokolski, 2023. "Assessment of Suitability for Long-Term Operation of a Bucket Elevator: A Case Study," Energies, MDPI, vol. 16(23), pages 1-16, November.

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