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A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant

Author

Listed:
  • Sara Antomarioni

    (Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, Via Brecce Bianche, 12, 60131 Ancona, Italy)

  • Marjorie Maria Bellinello

    (Department of Mechanical and Maintenance Engineering, Federal University of Technology of Paraná, 3165-Rebouças, Curitiba 80230-901, Brazil
    Department of Mechatronics and Mechanical Systems Engineering, USP—University of São Paulo, Avenida Professor Mello de Moraes 2231, São Paulo 05508-030, Brazil)

  • Maurizio Bevilacqua

    (Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, Via Brecce Bianche, 12, 60131 Ancona, Italy)

  • Filippo Emanuele Ciarapica

    (Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, Via Brecce Bianche, 12, 60131 Ancona, Italy)

  • Renan Favarão da Silva

    (Department of Mechatronics and Mechanical Systems Engineering, USP—University of São Paulo, Avenida Professor Mello de Moraes 2231, São Paulo 05508-030, Brazil)

  • Gilberto Francisco Martha de Souza

    (Department of Mechatronics and Mechanical Systems Engineering, USP—University of São Paulo, Avenida Professor Mello de Moraes 2231, São Paulo 05508-030, Brazil)

Abstract

Power plants are required to supply the electric demand efficiently, and appropriate failure analysis is necessary for ensuring their reliability. This paper proposes a framework to extend the failure analysis: indeed, the outcomes traditionally carried out through techniques such as the Failure Mode and Effects Analysis (FMEA) are elaborated through data-driven methods. In detail, the Association Rule Mining (ARM) is applied in order to define the relationships among failure modes and related characteristics that are likely to occur concurrently. The Social Network Analysis (SNA) is then used to represent and analyze these relationships. The main novelty of this work is represented by support in the maintenance management process based not only on the traditional failure analysis but also on a data-driven approach. Moreover, the visual representation of the results provides valuable support in terms of comprehension of the context to implement appropriate actions. The proposed approach is applied to the case study of a hydroelectric power plant, using real-life data.

Suggested Citation

  • Sara Antomarioni & Marjorie Maria Bellinello & Maurizio Bevilacqua & Filippo Emanuele Ciarapica & Renan Favarão da Silva & Gilberto Francisco Martha de Souza, 2020. "A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant," Energies, MDPI, vol. 13(23), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6400-:d:455755
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    References listed on IDEAS

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    1. Marek Stawowy & Adam Rosiński & Jacek Paś & Tomasz Klimczak, 2021. "Method of Estimating Uncertainty as a Way to Evaluate Continuity Quality of Power Supply in Hospital Devices," Energies, MDPI, vol. 14(2), pages 1-16, January.

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