RT info:eu-repo/semantics/article
T1 A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry
A1 Redondo Guevara, Raquel
A1 Herrero Cosío, Álvaro
A1 Corchado, Emilio
A1 Sedano, Javier
K1 Industry 4.0
K1 Industrial internet of things
K1 Smart factories
K1 Advanced manufacturing
K1 Industrial big data
K1 Predictive maintenance
K1 Visualization
K1 Machine learning
K1 Clustering
K1 Exploratory projection pursuit
K1 Informática
K1 Computer science
AB In recent years, the digital transformation has been advancing in industrial companies,supported by the Key Enabling Technologies (Big Data, IoT, etc.) of Industry 4.0. As a consequence,companies have large volumes of data and information that must be analyzed to give them competitiveadvantages. This is of the utmost importance in fields such as Failure Detection (FD) and PredictiveMaintenance (PdM). Finding patterns in such data is not easy, but cutting-edge technologies, such asMachine Learning (ML), can make great contributions. As a solution, this study extends HybridUnsupervised Exploratory Plots (HUEPs), as a visualization technique that combines ExploratoryProjection Pursuit (EPP) and Clustering methods. An extended formulation of HUEPs is proposed,adding for the first time the following EPP methods: Classical Multidimensional Scaling, SammonMapping and Factor Analysis. Extended HUEPs are validated in a case study associated with amultinational company in the automotive industry sector. Two real-life datasets containing datagathered from a Waterjet Cutting tool are visualized in an intuitive and informative way. The obtainedresults show that HUEPs is a technique that supports the continuous monitoring of machines in orderto anticipate failures. This contribution to visual data analytics can help companies in decision-making,regarding FD and PdM projects.
PB MDPI
YR 2020
FD 2020-06
LK http://hdl.handle.net/10259/7246
UL http://hdl.handle.net/10259/7246
LA eng
NO The authors would like to thank the vehicle interiors manufacturer, Grupo Antolin, for its collaboration in this research.
DS Repositorio Institucional de la Universidad de Burgos
RD 23-nov-2024