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A Framework for Big Data Analytical Process and Mapping—BAProM: Description of an Application in an Industrial Environment

Author

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  • Giovanni Gravito de Carvalho Chrysostomo

    (Postgraduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Rua da Consolação, 896, Prédio 30—Consolação, São Paulo 01302-907, Brazil)

  • Marco Vinicius Bhering de Aguiar Vallim

    (Postgraduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Rua da Consolação, 896, Prédio 30—Consolação, São Paulo 01302-907, Brazil)

  • Leilton Santos da Silva

    (EMAE—Metropolitan Company of Water & Energy, Avenida Nossa Senhora do Sabará, 5312—Vila Emir, São Paulo 04447-902, Brazil)

  • Leandro A. Silva

    (Postgraduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, Rua da Consolação, 896, Prédio 30—Consolação, São Paulo 01302-907, Brazil)

  • Arnaldo Rabello de Aguiar Vallim Filho

    (Computer Science Department, Mackenzie Presbyterian University, Rua da Consolação, 896, Prédio 31—Consolação, São Paulo 01302-907, Brazil)

Abstract

This paper presents an application of a framework for Big Data Analytical Process and Mapping—BAProM—consisting of four modules: Process Mapping, Data Management, Data Analysis, and Predictive Modeling. The framework was conceived as a decision support tool for industrial business, encompassing the whole big data analytical process. The first module incorporates in big data analytical a mapping of processes and variables, which is not common in such processes. This is a proposal that proved to be adequate in the practical application that was developed. Next, an analytical “workbench” was implemented for data management and exploratory analysis (Modules 2 and 3) and, finally, in Module 4, the implementation of artificial intelligence algorithm support predictive processes. The modules are adaptable to different types of industry and problems and can be applied independently. The paper presents a real-world application seeking as final objective the implementation of a predictive maintenance decision support tool in a hydroelectric power plant. The process mapping in the plant identified four subsystems and 100 variables. With the support of the analytical workbench, all variables have been properly analyzed. All underwent a cleaning process and many had to be transformed, before being subjected to exploratory analysis. A predictive model, based on a decision tree (DT), was implemented for predictive maintenance of equipment, identifying critical variables that define the imminence of an equipment failure. This DT model was combined with a time series forecasting model, based on artificial neural networks, to project those critical variables for a future time. The real-world application showed the practical feasibility of the framework, particularly the effectiveness of the analytical workbench, for pre-processing and exploratory analysis, as well as the combined predictive model, proving effectiveness by providing information on future events leading to equipment failures.

Suggested Citation

  • Giovanni Gravito de Carvalho Chrysostomo & Marco Vinicius Bhering de Aguiar Vallim & Leilton Santos da Silva & Leandro A. Silva & Arnaldo Rabello de Aguiar Vallim Filho, 2020. "A Framework for Big Data Analytical Process and Mapping—BAProM: Description of an Application in an Industrial Environment," Energies, MDPI, vol. 13(22), pages 1-28, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6014-:d:446828
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    References listed on IDEAS

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    3. Froger, Aurélien & Gendreau, Michel & Mendoza, Jorge E. & Pinson, Éric & Rousseau, Louis-Martin, 2016. "Maintenance scheduling in the electricity industry: A literature review," European Journal of Operational Research, Elsevier, vol. 251(3), pages 695-706.
    4. Liu, Bin & Xu, Zhengguo & Xie, Min & Kuo, Way, 2014. "A value-based preventive maintenance policy for multi-component system with continuously degrading components," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 83-89.
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    Cited by:

    1. Arnaldo Rabello de Aguiar Vallim Filho & Daniel Farina Moraes & Marco Vinicius Bhering de Aguiar Vallim & Leilton Santos da Silva & Leandro Augusto da Silva, 2022. "A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case," Energies, MDPI, vol. 15(10), pages 1-41, May.

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