A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry
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....
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/12/4355 |
| _version_ | 1797564214360932352 |
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| author | Raquel Redondo Álvaro Herrero Emilio Corchado Javier Sedano |
| author_facet | Raquel Redondo Álvaro Herrero Emilio Corchado Javier Sedano |
| author_sort | Raquel Redondo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T18:54:14Z |
| format | Article |
| id | doaj.art-585f64f45c1f4fc7bd006f050a4a6690 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T18:54:14Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-585f64f45c1f4fc7bd006f050a4a66902023-11-20T04:55:02ZengMDPI AGApplied Sciences2076-34172020-06-011012435510.3390/app10124355A Decision-Making Tool Based on Exploratory Visualization for the Automotive IndustryRaquel Redondo0Álvaro Herrero1Emilio Corchado2Javier Sedano3Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006 Burgos, SpainGrupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006 Burgos, SpainDepartamento de Informática y Automática, Universidad de Salamanca, Plaza de la Merced s/n, 37008 Salamanca, SpainInstituto Tecnológico de Castilla y León, Pol. Ind. Villalonquejar, C/López Bravo 70, 09001 Burgos, SpainIn 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.https://www.mdpi.com/2076-3417/10/12/4355industry 4.0industrial internet of thingssmart factoriesadvanced manufacturingindustrial big datapredictive maintenance |
| spellingShingle | Raquel Redondo Álvaro Herrero Emilio Corchado Javier Sedano A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry Applied Sciences industry 4.0 industrial internet of things smart factories advanced manufacturing industrial big data predictive maintenance |
| title | A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry |
| title_full | A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry |
| title_fullStr | A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry |
| title_full_unstemmed | A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry |
| title_short | A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry |
| title_sort | decision making tool based on exploratory visualization for the automotive industry |
| topic | industry 4.0 industrial internet of things smart factories advanced manufacturing industrial big data predictive maintenance |
| url | https://www.mdpi.com/2076-3417/10/12/4355 |
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