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A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History

Author

Listed:
  • Alejandro Blanco-M.

    (Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain)

  • Pere Marti-Puig

    (Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain)

  • Karina Gibert

    (Knowledge Engineering and Machine Learning Group at Intelligent Data Science and Artificial Intelligence Research Center (KEMLG-at-IDEAI), Polytechnic University of Catalonia, 08034 Barcelona, Catalonia, Spain)

  • Jordi Cusidó

    (Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain
    Smartive-ITESTIT SL, 08225 Terrassa, Catalonia, Spain)

  • Jordi Solé-Casals

    (Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain)

Abstract

Detecting and determining which systems or subsystems of a wind turbine have more failures is essential to improve their design, which will reduce the costs of generating wind power. Two of the most critical failures, the generator and gearbox, are analyzed and characterized with four metrics. This failure analysis usually begins with the identification of the turbine’s condition, a process normally performed by an expert examining the wind turbine’s service history. This is a time-consuming task, as a human expert has to examine each service entry. To automate this process, a new methodology is presented here, which is based on a set of steps to preprocess and decompose the service history to find relevant words and sentences that discriminate an unhealthy wind turbine period from a healthy one. This is achieved by means of two classifiers fed with the matrix of terms from the decomposed document of the training wind turbines. The classifiers can extract essential words and determine the conditions of new turbines of unknown status using the text from the service history, emulating what a human expert manually does when labelling the training set. Experimental results are promising, with accuracy and F-score above 90% in some cases. Condition monitoring system can be improved and automated using this system, which helps the expert in the tedious task of identifying the relevant words from the turbine service history. In addition, the system can be retrained when new knowledge becomes available and may therefore always be as accurate as a human expert. With this new tool, the expert can focus on identifying which systems or subsystems can be redesigned to increase the efficiency of wind turbines.

Suggested Citation

  • Alejandro Blanco-M. & Pere Marti-Puig & Karina Gibert & Jordi Cusidó & Jordi Solé-Casals, 2019. "A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History," Energies, MDPI, vol. 12(10), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1982-:d:233723
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    References listed on IDEAS

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    1. Md Liton Hossain & Ahmed Abu-Siada & S. M. Muyeen, 2018. "Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review," Energies, MDPI, vol. 11(5), pages 1-14, May.
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    4. Küçük, Dilek & Arslan, Yusuf, 2014. "Semi-automatic construction of a domain ontology for wind energy using Wikipedia articles," Renewable Energy, Elsevier, vol. 62(C), pages 484-489.
    5. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
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    Citations

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    Cited by:

    1. Giovanni Rinaldi & Philipp R. Thies & Lars Johanning, 2021. "Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 14(9), pages 1-28, April.
    2. Gürdal Ertek & Lakshmi Kailas, 2021. "Analyzing a Decade of Wind Turbine Accident News with Topic Modeling," Sustainability, MDPI, vol. 13(22), pages 1-34, November.
    3. Mingzhu Tang & Wei Chen & Qi Zhao & Huawei Wu & Wen Long & Bin Huang & Lida Liao & Kang Zhang, 2019. "Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data," Energies, MDPI, vol. 12(17), pages 1-15, September.
    4. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Silvio Simani & Saverio Farsoni & Paolo Castaldi, 2023. "RETRACTED: Supervisory Control and Data Acquisition for Fault Diagnosis of Wind Turbines via Deep Transfer Learning," Energies, MDPI, vol. 16(9), pages 1-22, April.
    6. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    7. Kai Chen & Rabea Jamil Mahfoud & Yonghui Sun & Dongliang Nan & Kaike Wang & Hassan Haes Alhelou & Pierluigi Siano, 2020. "Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM," Energies, MDPI, vol. 13(17), pages 1-17, September.

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