2026-06-23T17:55:29Zhttps://riubu.ubu.es/oai/request

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

00925njm 22002777a 4500 dc Redondo Guevara, Raquel author Herrero Cosío, Álvaro author Corchado, Emilio author Sedano, Javier author 2020-06 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. http://hdl.handle.net/10259/7246 10.3390/app10124355 2076-3417 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