2026-04-19T06:20:37Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72462023-01-18T01:05:20Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry Redondo Guevara, Raquel Herrero Cosío, Álvaro Corchado, Emilio Sedano, Javier Industry 4.0 Industrial internet of things Smart factories Advanced manufacturing Industrial big data Predictive maintenance Visualization Machine learning Clustering Exploratory projection pursuit Informática Computer science 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 competitive advantages. This is of the utmost importance in fields such as Failure Detection (FD) and Predictive Maintenance (PdM). Finding patterns in such data is not easy, but cutting-edge technologies, such as Machine Learning (ML), can make great contributions. As a solution, this study extends Hybrid Unsupervised Exploratory Plots (HUEPs), as a visualization technique that combines Exploratory Projection Pursuit (EPP) and Clustering methods. An extended formulation of HUEPs is proposed, adding for the first time the following EPP methods: Classical Multidimensional Scaling, Sammon Mapping and Factor Analysis. Extended HUEPs are validated in a case study associated with a multinational company in the automotive industry sector. Two real-life datasets containing data gathered from a Waterjet Cutting tool are visualized in an intuitive and informative way. The obtained results show that HUEPs is a technique that supports the continuous monitoring of machines in order to anticipate failures. This contribution to visual data analytics can help companies in decision-making, regarding FD and PdM projects. The authors would like to thank the vehicle interiors manufacturer, Grupo Antolin, for its collaboration in this research. 2023-01-17T07:45:05Z 2023-01-17T07:45:05Z 2020-06 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/10259/7246 10.3390/app10124355 2076-3417 eng Applied sciences. 2020, V. 10, n. 12, e4355 https://doi.org/10.3390/app10124355 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess application/pdf MDPI