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

• Main Authors: Giovanni Gravito de Carvalho Chrysostomo, Marco Vinicius Bhering de Aguiar Vallim, Leilton Santos da Silva, Leandro A. Silva, Arnaldo Rabello de Aguiar Vallim Filho
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Energies
• Subjects: big data process; predictive maintenance; machine learning
• Online Access: https://www.mdpi.com/1996-1073/13/22/6014

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.