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.
Highlights
Interest in data-based knowledge applied to decision-making processes has been growing in different industrial segments [1]
The practical application strictly followed the modules of the BAProM framework, which are presented in the following subsections
Before describing this last module of the framework is important to point out once more, the general objective of this application, which is to support the predictive maintenance decisions, by a minimization of corrective maintenance occurrence, and so, the objective of this application is to support predictive maintenance decisions, identifying when an equipment is in the imminence of a failure
Summary
Interest in data-based knowledge applied to decision-making processes has been growing in different industrial segments [1]. The importance of this movement of data-driven decisions is understood, since organizations with better performance have used data analysis five times more than those with low performance [2]. This movement of implementing a so-called KDD—knowledge discovery in databases—environment is relatively new in industrial business, and it is due, on the one hand, to the huge volume of data generated (big data), which is largely the result of the Internet of Things (IoT), where sensors connected to a variety of objects, spread across the planet, have accelerated the big data phenomenon. In this type of maintenance rather than scheduling operation suspension for maintenance, based on fixed time intervals, the best stopping moment is defined based on AI inference, as a result of an analytical model, calibrated (trained) on the basis of historical data [4,5,6]
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