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Alternative fault detection and diagnostic using information theory quantifiers based on vibration time-waveforms from condition monitoring systems: Application to operational wind turbines

Author

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  • 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
  • Rosso, Osvaldo Anibal

Abstract

Wind turbines operate almost uninterruptedly, and their operation is often subject to harsh environments, as well as complex and dynamic loads. Fourier analysis, a standard diagnostic technique, presents some limitations regarding the use of non-stationary, non-periodic, noisy data, which is precisely the case with wind turbine data. Due to these limitations, unseen faults could progress and cause severe, and even catastrophic, failure in wind turbines. Information theory quantifiers, such as entropy, divergence, and, statistical complexity measure, are proposed to evaluate the health status of wind turbine components. In this work, this is done via the decomposition of the signal in time, frequency, and time-frequency domain, namely via Bandt and Pompe, power spectrum, and wavelet packet decomposition. Two different real data sets from operational wind turbines were characterized by the proposed methods. Results demonstrate that the proposed method can distinguish (cluster) well between the states of fault, but also presented some limitations, mainly related to the complexity of the signal from operational wind turbines. Based on these results, new methods, complementary to Fourier analysis, are proposed to be employed in wind turbine data, aiming to increase the capability of detecting faults in such a complex environment.

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  • 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.
    • Handle: RePEc:eee:renene:v:164:y:2021:i:c:p:1183-1194
    DOI: 10.1016/j.renene.2020.10.129
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    References listed on IDEAS

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    1. Leite, Gustavo de Novaes Pires & Araújo, Alex Maurício & Rosas, Pedro André Carvalho & Stosic, Tatijana & Stosic, Borko, 2019. "Entropy measures for early detection of bearing faults," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 458-472.
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    Cited by:

    1. Xing, Zuoxia & Chen, Mingyang & Cui, Jia & Chen, Zhe & Xu, Jian, 2022. "Detection of magnitude and position of rotor aerodynamic imbalance of wind turbines using Convolutional Neural Network," Renewable Energy, Elsevier, vol. 197(C), pages 1020-1033.
    2. 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.
    3. Wang, Anqi & Qian, Zheng & Pei, Yan & Jing, Bo, 2022. "A de-ambiguous condition monitoring scheme for wind turbines using least squares generative adversarial networks," Renewable Energy, Elsevier, vol. 185(C), pages 267-279.
    4. Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.

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