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

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.

Data mining in predictive maintenance systems: A taxonomy and systematic review

“…These records are extracted later or in real time to apply PdM. Examples of this case are found on the one hand in industrial processes (Bampoula et al, 2021; Bekar et al, 2020; Chang et al, 2021; Kim et al, 2021; Kolokas et al, 2020; Lepenioti et al, 2020; Li et al, 2017; Morariu et al, 2020; Rafique et al, 2018; Ruiz‐Sarmiento et al, 2020; Susto et al, 2015; Uhlmann et al, 2018; Zschech et al, 2019), in production lines (Ayvaz & Alpay, 2021; Azab et al, 2021; Cerquitelli et al, 2021; Fathi et al, 2021; Giordano et al, 2021; Liu et al, 2021), in power plants (de Carvalho Chrysostomo et al, 2020; Khodabakhsh et al, 2018; Sun et al, 2021; Zhang, Liu, et al, 2018), in wind turbines (Chen, Hsu, et al, 2021; Leahy et al, 2018; Santolamazza et al, 2021), in ventilation systems (Fernandes et al, 2020), cryogenic pumps (Crespo Márquez et al, 2020), heat meters (Pałasz & Przysowa, 2019), press machines (Serradilla et al, 2021), or water treatment plants (Srivastava et al, 2018). Another common scenario for PdM is related to transportation: different types of land vehicles (Chen et al, 2020; Patil et al, 2021; Prytz et al, 2015; Shafi et al, 2018), aircrafts (Baptista et al, 2021; Basora et al, 2021; Ning et al, 2021; Savitha et al, 2020; Yang et al, 2017), and naval ships (Berghout et al, 2021; Fernández‐Barrero et al, 2021; Gribbestad et al, 2021) have been monitored through their electronic control units.…”

“…DTs are very popular in classification PdM problems because of their properties related to interpretability and explainability, very useful to provide extra information about the possible causes of the predicted failure. Thus, in (de Carvalho Chrysostomo et al (2020), a DT is applied to the output of a future signal estimation to provide an automatically decision support system. Other study case is shown in Aremu, Hyland‐Wood, and McAree (2020) where the DT is compared with other models to classify normal and failure records after dimensionality reduction, achieving the best performance.…”

“…The interest in PdM has result in numerous research works in last decade, and specifically from 2018, that apply DM predictive techniques to different applications: motors (Aremu, Hyland‐Wood, & McAree, 2020; Pang et al, 2020; Wang, Zhang, et al, 2020), manufacturing plants (Axenie et al, 2020; Yu et al, 2020), medical equipment (Shamayleh et al, 2020), energy production plants (de Carvalho Chrysostomo et al, 2020; Gohel et al, 2020) or vehicle fleets (C. Chen et al, 2020; Oh & Lee, 2020) among others. However, there is not a direct application of DM to PdM.…”

Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode

“…The qualitative leap that accompanies the fourth industrial revolution results in humans and machines co-creating a production system [ 15 ]. In this system, various components can be distinguished: Incremental manufacturing (3D printing)—used to produce complete products [ 16 , 17 , 18 ], systems integration—the flow of data and control signals between machines, information systems, people and management systems [ 19 , 20 , 21 , 22 ], Industrial Internet of Things (IIoT)—the unified integration of statuary and management systems [ 23 , 24 ], Cloud computing—making external hardware and software resources available to process production data and perform complex calculations for the production process itself as well as for management systems [ 25 , 26 , 27 ], big data analysis (Big Data)—i.e., the use of large computing powers to collect, store and analyze large amounts of data [ 28 , 29 ], augmented and virtual reality (AR and VR) [ 30 , 31 , 32 ]—technologies that are proposing to move industry to virtual and augmented reality, Cyber security—which plays a huge role in cloud computing and analyzing large data sets on external servers [ 33 , 34 ]. …”

“…big data analysis (Big Data)—i.e., the use of large computing powers to collect, store and analyze large amounts of data [ 28 , 29 ],…”