Anomaly detection based on intelligent techniques over a bicomponent production plant used on wind generator blades manufacturing
Technological advances, especially in the industrial field, have led to the development and optimization of the activities that takes place on it. To achieve this goal, an early detection of any kind of anomaly is very important. This can contribute to energy and economic savings and an environmenta...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | Spanish |
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Universitat Politècnica de València
2020-01-01
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| Series: | Revista Iberoamericana de Automática e Informática Industrial RIAI |
| Subjects: | |
| Online Access: | https://polipapers.upv.es/index.php/RIAI/article/view/11055 |
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| author | E. Jove J. Casteleiro-Roca H. Quintián J. A. Méndez-Pérez J. L. Calvo-Rolle |
| author_facet | E. Jove J. Casteleiro-Roca H. Quintián J. A. Méndez-Pérez J. L. Calvo-Rolle |
| author_sort | E. Jove |
| collection | DOAJ |
| description | Technological advances, especially in the industrial field, have led to the development and optimization of the activities that takes place on it. To achieve this goal, an early detection of any kind of anomaly is very important. This can contribute to energy and economic savings and an environmental impact reduction. In a context where the reduction of pollution gasses emission is promoted, the use of alternative energies, specially the wind energy, plays a key role. The wind generator blades are usually manufactured from bicomponent material, obtained from the mixture of two dierent primary components. The present research assesses dierent one-class intelligent techniques to perform anomaly detection on a bicomponent mixing system used on the wind generator manufacturing. To perform the anomaly detection, the intelligent models were obtained from real dataset recorded during the right operation of a bicomponent mixing plant. The classifiers for each technique were validated using artificial outliers, achieving very good results. |
| format | Article |
| id | doaj-art-47335347ba4d48759a8a6f54bbe2198c |
| institution | Kabale University |
| issn | 1697-7912 1697-7920 |
| language | Spanish |
| publishDate | 2020-01-01 |
| publisher | Universitat Politècnica de València |
| record_format | Article |
| series | Revista Iberoamericana de Automática e Informática Industrial RIAI |
| spelling | doaj-art-47335347ba4d48759a8a6f54bbe2198c2024-12-02T02:38:12ZspaUniversitat Politècnica de ValènciaRevista Iberoamericana de Automática e Informática Industrial RIAI1697-79121697-79202020-01-01171849310.4995/riai.2019.110557621Anomaly detection based on intelligent techniques over a bicomponent production plant used on wind generator blades manufacturingE. Jove0J. Casteleiro-Roca1H. Quintián2J. A. Méndez-Pérez3J. L. Calvo-Rolle4Univesidade da Coruña,Univesidade da CoruñaUnivesidade da CoruñaUniversidad de La LagunaUnivesidade da CoruñaTechnological advances, especially in the industrial field, have led to the development and optimization of the activities that takes place on it. To achieve this goal, an early detection of any kind of anomaly is very important. This can contribute to energy and economic savings and an environmental impact reduction. In a context where the reduction of pollution gasses emission is promoted, the use of alternative energies, specially the wind energy, plays a key role. The wind generator blades are usually manufactured from bicomponent material, obtained from the mixture of two dierent primary components. The present research assesses dierent one-class intelligent techniques to perform anomaly detection on a bicomponent mixing system used on the wind generator manufacturing. To perform the anomaly detection, the intelligent models were obtained from real dataset recorded during the right operation of a bicomponent mixing plant. The classifiers for each technique were validated using artificial outliers, achieving very good results.https://polipapers.upv.es/index.php/RIAI/article/view/11055sistemas de energías renovablesaerogeneradoresdetección de anomalíasdiagnóstico de sistemasredes neuronales |
| spellingShingle | E. Jove J. Casteleiro-Roca H. Quintián J. A. Méndez-Pérez J. L. Calvo-Rolle Anomaly detection based on intelligent techniques over a bicomponent production plant used on wind generator blades manufacturing Revista Iberoamericana de Automática e Informática Industrial RIAI sistemas de energías renovables aerogeneradores detección de anomalías diagnóstico de sistemas redes neuronales |
| title | Anomaly detection based on intelligent techniques over a bicomponent production plant used on wind generator blades manufacturing |
| title_full | Anomaly detection based on intelligent techniques over a bicomponent production plant used on wind generator blades manufacturing |
| title_fullStr | Anomaly detection based on intelligent techniques over a bicomponent production plant used on wind generator blades manufacturing |
| title_full_unstemmed | Anomaly detection based on intelligent techniques over a bicomponent production plant used on wind generator blades manufacturing |
| title_short | Anomaly detection based on intelligent techniques over a bicomponent production plant used on wind generator blades manufacturing |
| title_sort | anomaly detection based on intelligent techniques over a bicomponent production plant used on wind generator blades manufacturing |
| topic | sistemas de energías renovables aerogeneradores detección de anomalías diagnóstico de sistemas redes neuronales |
| url | https://polipapers.upv.es/index.php/RIAI/article/view/11055 |
| work_keys_str_mv | AT ejove anomalydetectionbasedonintelligenttechniquesoverabicomponentproductionplantusedonwindgeneratorbladesmanufacturing AT jcasteleiroroca anomalydetectionbasedonintelligenttechniquesoverabicomponentproductionplantusedonwindgeneratorbladesmanufacturing AT hquintian anomalydetectionbasedonintelligenttechniquesoverabicomponentproductionplantusedonwindgeneratorbladesmanufacturing AT jamendezperez anomalydetectionbasedonintelligenttechniquesoverabicomponentproductionplantusedonwindgeneratorbladesmanufacturing AT jlcalvorolle anomalydetectionbasedonintelligenttechniquesoverabicomponentproductionplantusedonwindgeneratorbladesmanufacturing |