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

Sara Antomarioni, Marjorie Maria Bellinello, Maurizio Bevilacqua, Filippo Emanuele Ciarapica, Renan Favarão da Silva and Gilberto Francisco Martha de Souza
Additional contact information
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
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

Energies, 2020, vol. 13, issue 23, 1-16

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.

Keywords: maintenance; hydroelectric power plant; reliability; data-driven; association rule; data mining (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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