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Fault Detection and Diagnosis Based on Unsupervised Machine Learning Methods: A Kaplan Turbine Case Study

Author

Listed:
  • Miguel A. C. Michalski

    (Department of Mechatronics and Mechanical System Engineering, Polytechnic School of the University of São Paulo, São Paulo 05508-030, SP, Brazil)

  • Arthur H. A. Melani

    (Department of Mechatronics and Mechanical System Engineering, Polytechnic School of the University of São Paulo, São Paulo 05508-030, SP, Brazil)

  • Renan F. da Silva

    (Department of Mechatronics and Mechanical System Engineering, Polytechnic School of the University of São Paulo, São Paulo 05508-030, SP, Brazil)

  • Gilberto F. M. de Souza

    (Department of Mechatronics and Mechanical System Engineering, Polytechnic School of the University of São Paulo, São Paulo 05508-030, SP, Brazil)

  • Fernando H. Hamaji

    (EDP Brasil, Rua Gomes de Carvalho, 1996—Vila Olímpia, São Paulo 04547-006, SP, Brazil)

Abstract

From the breakdown of the Kaplan rotor of a hydrogenerator unit and the monitored data collected during its operation before such a failure, this work presents a post-occurrence data analysis in which a previously developed hybrid method based on unsupervised machine learning techniques is applied to detect and diagnose failure before a unit shutdown. In addition to demonstrating the efficiency and capacity of the developed method in an application with real data, the conducted analysis seeks to shed light on the events that occurred at the considered hydroelectric power plant, helping to understand the failure mode evolution and outcome. The results of the fault detection and diagnosis process clearly demonstrated how the evolution of failure modes took place in the analyzed equipment. The detection of potential failures far in advance would support adequate maintenance planning and mitigating actions that could prevent unit breakdown and the consequent damage and financial losses.

Suggested Citation

  • Miguel A. C. Michalski & Arthur H. A. Melani & Renan F. da Silva & Gilberto F. M. de Souza & Fernando H. Hamaji, 2021. "Fault Detection and Diagnosis Based on Unsupervised Machine Learning Methods: A Kaplan Turbine Case Study," Energies, MDPI, vol. 15(1), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:15:y:2021:i:1:p:80-:d:709248
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    References listed on IDEAS

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    1. Maria Holgado & Marco Macchi & Stephen Evans, 2020. "Exploring the impacts and contributions of maintenance function for sustainable manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(23), pages 7292-7310, December.
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