2026-06-23T13:29:55Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/72462023-01-18T01:05:20Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
Repositorio Institucional de la Universidad de Burgos
author
Redondo Guevara, Raquel
author
Herrero Cosío, Álvaro
author
Corchado, Emilio
author
Sedano, Javier
2023-01-17T07:45:05Z
2023-01-17T07:45:05Z
2020-06
http://hdl.handle.net/10259/7246
10.3390/app10124355
2076-3417
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.
eng
Atribución 4.0 Internacional
Industry 4.0
Industrial internet of things
Smart factories
Advanced manufacturing
Industrial big data
Predictive maintenance
Visualization
Machine learning
Clustering
Exploratory projection pursuit
A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/7246/1/Redondo-as_2020.pdf
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Redondo-as_2020.pdf