- -

Detección de anomalías basada en técnicas inteligentes de una planta de obtención de material bicomponente empleado en la fabricación de palas de aerogenerador

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Detección de anomalías basada en técnicas inteligentes de una planta de obtención de material bicomponente empleado en la fabricación de palas de aerogenerador

Mostrar el registro completo del ítem

Jove, E.; Casteleiro-Roca, J.; Quintián, H.; Méndez-Pérez, JA.; Calvo-Rolle, JL. (2020). Detección de anomalías basada en técnicas inteligentes de una planta de obtención de material bicomponente empleado en la fabricación de palas de aerogenerador. Revista Iberoamericana de Automática e Informática industrial. 17(1):84-93. https://doi.org/10.4995/riai.2019.11055

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/138327

Ficheros en el ítem

Thumbnail
Nombre: Jove;Casteleiro-R ...
Tamaño: 1.017Mb
Formato: PDF
Descripción: Versión editorial
Abrir/Preview

Metadatos del ítem

Título: Detección de anomalías basada en técnicas inteligentes de una planta de obtención de material bicomponente empleado en la fabricación de palas de aerogenerador
Otro titulo: Anomaly detection based on intelligent techniques over a bicomponent production plant used on wind generator blades manufacturing
Autor: Jove, E. Casteleiro-Roca, J. Quintián, H. Méndez-Pérez, J. A. Calvo-Rolle, J. L.
Fecha difusión:
Resumen:
[ES] Los avances tecnológicos en general, y en el ámbito de la industria en particular, conllevan el desarrollo y optimización de las actividades que en ella tienen lugar. Para alcanzar este objetivo, resulta de vital ...[+]


[EN] 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 ...[+]
Palabras clave: Renewable energy systems , Windmills , Fault detection , System diagnosis , Neural networks , Sistemas de energías renovables , Aerogeneradores , Detección de anomalías , Diagnóstico de sistemas , Redes neuronales
Derechos de uso: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Fuente:
Revista Iberoamericana de Automática e Informática industrial. (issn: 1697-7912 ) (eissn: 1697-7920 )
DOI: 10.4995/riai.2019.11055
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.4995/riai.2019.11055
Tipo: Artículo

References

Bradley, A. P., 1997. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition 30 (7), 1145 - 1159. https://doi.org/10.1016/S0031-3203(96)00142-2

Casale, P., Pujol, O., Radeva, P., 2011. Approximate convex hulls family for one-class classification. In: Sansone, C., Kittler, J., Roli, F. (Eds.), Multiple Classifier Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 106-115. https://doi.org/10.1007/978-3-642-21557-5_13

Casale, P., Pujol, O., Radeva, P., 2014. Approximate polytope ensemble for oneclass classification. Pattern Recognition 47 (2), 854 - 864. https://doi.org/10.1016/j.patcog.2013.08.007 [+]
Bradley, A. P., 1997. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition 30 (7), 1145 - 1159. https://doi.org/10.1016/S0031-3203(96)00142-2

Casale, P., Pujol, O., Radeva, P., 2011. Approximate convex hulls family for one-class classification. In: Sansone, C., Kittler, J., Roli, F. (Eds.), Multiple Classifier Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 106-115. https://doi.org/10.1007/978-3-642-21557-5_13

Casale, P., Pujol, O., Radeva, P., 2014. Approximate polytope ensemble for oneclass classification. Pattern Recognition 47 (2), 854 - 864. https://doi.org/10.1016/j.patcog.2013.08.007

Chandola, V., Banerjee, A., Kumar, V., 2009. Anomaly detection: A survey. ACM computing surveys (CSUR) 41 (3), 15. https://doi.org/10.1145/1541880.1541882

Chen, Y., Zhou, X. S., Huang, T. S., 2001. One-class svm for learning in image retrieval. In: Image Processing, 2001. Proceedings. 2001 International Conference on. Vol. 1. IEEE, pp. 34-37.

Chiang, L. H., Russell, E. L., Braatz, R. D., 2000. Fault detection and diagnosis in industrial systems. Springer Science & Business Media.

de la Portilla, M. P., Piñeiro, A. L., Sánchez, J. A. S., Herrera, R. M., 2017. Modelado dinámico y control de un dispositivo sumergido provisto de actuadores hidrostáticos. Revista Iberoamericana de Automtica e Informática industrial 15 (1), 12-23. https://doi.org/10.4995/riai.2017.8824

Fan, H.,Wong, C., Yuen, M.-F., April 2006. Prediction of material properties of epoxy materials using molecular dynamic simulation. In: Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems, 2006. EuroSime 2006. 7th International Conference on. pp. 1-4. https://doi.org/10.1109/ESIME.2006.1644033

Fernández-Francos, D., Fontenla-Romero, O., Alonso-Betanzos, A., 2018. One-class convex hull-based algorithm for classification in distributed environments. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1-11. https://doi.org/10.1109/TSMC.2017.2771341

González, G., Angelo, C. D., Forchetti, D., Aligia, D., 2018. Diagnósico de fallas en el convertidor del rotor en generadores de inducción con rotor bobinado. Revista Iberoamericana de Automática e Informática industrial 15 (3), 297-308. https://doi.org/10.4995/riai.2017.9042

Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y., 2016. Deep learning. Vol. 1. MIT press Cambridge.

Heller, K. A., Svore, K. M., Keromytis, A. D., Stolfo, S. J., 2003. One class support vector machines for detecting anomalous windows registry accesses. In: Proc. of the workshop on Data Mining for Computer Security. Vol. 9.

Hobday, M., 1998. Product complexity, innovation and industrial organisation. Research policy 26 (6), 689-710. https://doi.org/10.1016/S0048-7333(97)00044-9

Hodge, V., Austin, J., 2004. A survey of outlier detection methodologies. Artificial intelligence review 22 (2), 85-126. https://doi.org/10.1023/B:AIRE.0000045502.10941.a9

Hwang, B., Cho, S., 1999. Characteristics of auto-associative mlp as a novelty detector. In: Neural Networks, 1999. IJCNN'99. International Joint Conference on. Vol. 5. IEEE, pp. 3086-3091.

Jove, E., Casteleiro-Roca, J.-L., Quintián, H., Méndez-Pérez, J. A., Calvo-Rolle, J. L., 2018. A new approach for system malfunctioning over an industrial system control loop based on unsupervised techniques. In: Graña, M., López-Guede, J. M., Etxaniz, O., Herrero, Á., Sáez, J. A., Quintián, H., Corchado, E. (Eds.), International Joint Conference SOCO'18-CISIS'18- ICEUTE'18. Springer International Publishing, Cham, pp. 415-425. https://doi.org/10.1007/978-3-319-94120-2_40

Krstajic, D., Buturovic, L. J., Leahy, D. E., Thomas, S., Mar 2014. Crossvalidation pitfalls when selecting and assessing regression and classification models. Journal of Cheminformatics 6 (1), 10. URL: https://doi.org/10.1186/1758-2946-6-10 https://doi.org/10.1186/1758-2946-6-10

Li, K.-L., Huang, H.-K., Tian, S.-F., Xu, W., 2003. Improving one-class svm for anomaly detection. In: Machine Learning and Cybernetics, 2003 International Conference on. Vol. 5. IEEE, pp. 3077-3081.

Miljkovic, D., 2011. Fault detection methods: A literature survey. In: MIPRO, 2011 proceedings of the 34th international convention. IEEE, pp. 750-755.

Sakurada, M., Yairi, T., 2014. Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis. ACM, p. 4 https://doi.org/10.1145/2689746.2689747

Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., Williamson, R. C., 2001. Estimating the support of a high-dimensional distribution. Neural computation 13 (7), 1443-1471. https://doi.org/10.1162/089976601750264965

Schwartz, J., 1994. Air pollution and daily mortality: A review and meta analysis. Environmental Research 64 (1), 36 - 52. https://doi.org/10.1006/enrs.1994.1005

Shalabi, L. A., Shaaban, Z., May 2006. Normalization as a preprocessing engine for data mining and the approach of preference matrix. In: 2006 International Conference on Dependability of Computer Systems. pp. 207-214. https://doi.org/10.1109/DEPCOS-RELCOMEX.2006.38

Tax, D., Jan 2018. Ddtools, the data description toolbox for matlab. Version 2.1.3.

Tax, D. M. J., 2001. One-class classification: concept-learning in the absence of counter-examples [ph. d. thesis]. Delft University of Technology.

Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A., 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research 11 (Dec), 3371-3408.

Wei, X., Huang, G., Li, Y., Aug 2007. Mahalanobis ellipsoidal learning machine for one class classification. In: 2007 International Conference on Machine Learning and Cybernetics. Vol. 6. pp. 3528-3533. https://doi.org/10.1109/ICMLC.2007.4370758

Westerhuis, J. A., Gurden, S. P., Smilde, A. K., 2000. Generalized contribution plots in multivariate statistical process monitoring. Chemometrics and intelligent laboratory systems 51 (1), 95-114. https://doi.org/10.1016/S0169-7439(00)00062-9

Wu, J., Zhang, X., 2001. A pca classifier and its application in vehicle detection. In: IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No. 01CH37222). Vol. 1. IEEE, pp. 600-604.

Young, W.-B., Wu, W.-H., Aug 2011. Optimization of the skin thickness distribution in the composite wind turbine blade. In: Fluid Power and Mechatronics (FPM), 2011 International Conference on. pp. 62-66. https://doi.org/10.1109/FPM.2011.6045730

Zeng, Z., Wang, J., 2010. Advances in neural network research and applications, 1st Edition. Springer Publishing Company, Incorporated. https://doi.org/10.1007/978-3-642-12990-2

Zuo, Y., Liu, H., June 2012. Evaluation on comprehensive benefit of wind power generation and utilization of wind energy. In: Software Engineering and Service Science (ICSESS), 2012 IEEE 3rd International Conference on. pp. 635-638. https://doi.org/10.1109/ICSESS.2012.6269547

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem