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Article

A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry

1
Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006 Burgos, Spain
2
Departamento de Informática y Automática, Universidad de Salamanca, Plaza de la Merced s/n, 37008 Salamanca, Spain
3
Instituto Tecnológico de Castilla y León, Pol. Ind. Villalonquejar, C/López Bravo 70, 09001 Burgos, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(12), 4355; https://doi.org/10.3390/app10124355
Received: 24 May 2020 / Revised: 21 June 2020 / Accepted: 22 June 2020 / Published: 25 June 2020
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. View Full-Text
Keywords: industry 4.0; industrial internet of things; smart factories; advanced manufacturing; industrial big data; predictive maintenance; visualization; machine learning; clustering; exploratory projection pursuit industry 4.0; industrial internet of things; smart factories; advanced manufacturing; industrial big data; predictive maintenance; visualization; machine learning; clustering; exploratory projection pursuit
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MDPI and ACS Style

Redondo, R.; Herrero, Á.; Corchado, E.; Sedano, J. A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry. Appl. Sci. 2020, 10, 4355. https://doi.org/10.3390/app10124355

AMA Style

Redondo R, Herrero Á, Corchado E, Sedano J. A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry. Applied Sciences. 2020; 10(12):4355. https://doi.org/10.3390/app10124355

Chicago/Turabian Style

Redondo, Raquel; Herrero, Álvaro; Corchado, Emilio; Sedano, Javier. 2020. "A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry" Appl. Sci. 10, no. 12: 4355. https://doi.org/10.3390/app10124355

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