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A Wind Turbine Vibration Monitoring System for Predictive Maintenance Based on Machine Learning Methods Developed under Safely Controlled Laboratory Conditions

Author

Listed:
  • David Pérez Granados

    (Engineering Department, CIIDETEC-Coyoacán, Universidad del Valle de México, Coyoacán 04910, Mexico)

  • Mauricio Alberto Ortega Ruiz

    (Engineering Department, CIIDETEC-Coyoacán, Universidad del Valle de México, Coyoacán 04910, Mexico
    Research Centre for Biomedical Engineering, City, University of London, London EC1V 0HB, UK)

  • Joel Moreira Acosta

    (Engineering Department, CIIDETEC-Tuxtla, Universidad del Valle de México, Tuxtla 29056, Mexico)

  • Sergio Arturo Gama Lara

    (Engineering Department, CIIDETEC-Toluca, Universidad del Valle de México, Toluca 52164, Mexico)

  • Roberto Adrián González Domínguez

    (Engineering Department, CIIDETEC-Tuxtla, Universidad del Valle de México, Tuxtla 29056, Mexico)

  • Pedro Jacinto Páramo Kañetas

    (Engineering Department, CIIDETEC-Coyoacán, Universidad del Valle de México, Coyoacán 04910, Mexico)

Abstract

Wind energy is one of the most relevant clean energies today, so wind turbines must have good health and be reliable in operation. Current wind turbines have slender and elastic structures that can be easily damaged through vibrations and compromise their health; therefore, vibration monitoring is essential to ensure safe operation. Here, we present a method for simple wind turbine vibration monitoring in the laboratory by means of an accelerometer placed on a weathervane under different scenarios, with recording of different amplitudes of vibrations caused at a constant speed of 10 km/h. The variables, trends, and data captured during vibration monitoring were then used to implement a prediction system of synthetic failure using machine learning methods such as: Medium Trees, Cubic SVN, Logistic Regression Kernel, Optimized Neural Network, and Bagged Trees, with the last demonstrating an accuracy of up to 0.87%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2290-:d:1082146
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    References listed on IDEAS

    as
    1. Angela Meyer, 2022. "Vibration Fault Diagnosis in Wind Turbines Based on Automated Feature Learning," Energies, MDPI, vol. 15(4), pages 1-13, February.
    2. Krzysztof Lalik & Filip Wątorek, 2021. "Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles," Energies, MDPI, vol. 14(22), pages 1-18, November.
    3. de Novaes Pires Leite, Gustavo & da Cunha, Guilherme Tenório Maciel & dos Santos Junior, José Guilhermino & Araújo, Alex Maurício & Rosas, Pedro André Carvalho & Stosic, Tatijana & Stosic, Borko & Ros, 2021. "Alternative fault detection and diagnostic using information theory quantifiers based on vibration time-waveforms from condition monitoring systems: Application to operational wind turbines," Renewable Energy, Elsevier, vol. 164(C), pages 1183-1194.
    4. Younis M. Nsaif & Molla Shahadat Hossain Lipu & Aini Hussain & Afida Ayob & Yushaizad Yusof & Muhammad Ammirrul A. M. Zainuri, 2022. "A New Voltage Based Fault Detection Technique for Distribution Network Connected to Photovoltaic Sources Using Variational Mode Decomposition Integrated Ensemble Bagged Trees Approach," Energies, MDPI, vol. 15(20), pages 1-20, October.
    5. João Pacheco & Gustavo Oliveira & Filipe Magalhães & Carlos Moutinho & Álvaro Cunha, 2021. "Vibration-Based Monitoring of Wind Turbines: Influence of Layout and Noise of Sensors," Energies, MDPI, vol. 14(2), pages 1-19, January.
    6. Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(C).
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