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dc.contributor.author | Trutié-Carrero, Eduardo | es_ES |
dc.contributor.author | Valdés-Santiago, Damian | es_ES |
dc.contributor.author | León-Mecías, Ángela | es_ES |
dc.contributor.author | Ramírez-Beltrán, Jorge | es_ES |
dc.date.accessioned | 2020-05-14T06:56:03Z | |
dc.date.available | 2020-05-14T06:56:03Z | |
dc.date.issued | 2018-03-05 | |
dc.identifier.issn | 1697-7912 | |
dc.identifier.uri | http://hdl.handle.net/10251/143156 | |
dc.description.abstract | [ES] La ruptura súbita en los sistemas de distribución de agua provoca gran pérdida de este recurso natural, interrumpe el abastecimiento, daña las calles y edificaciones y aumenta la transmisión de enfermedades infecciosas. En este artículo se propone un nuevo algoritmo que permite la detección y localización automática de rupturas súbitas en los sistemas de distribución de agua. En cuanto a la detección, la novedad consiste en usar el criterio de correlación wavelet para computar la decisión estadística y compararla con un umbral de detección. La novedad en la localización consiste en usar el operador estadístico correlación cruzada. El algoritmo se implementó en Octave y fue validado con 32 señales adquiridas en el laboratorio en una tubería de acero de 26.7 m de longitud. En 16 señales se provocó ruptura súbita las cuales fueron detectadas bajo una probabilidad de falsos positivos de 2 %. No se presentaron falsos positivos en las 16 señales donde solamente estaba la presencia de ruido. | es_ES |
dc.description.abstract | [EN] Burst in water distribution systems causes great loss of this natural resource, interrupts the water supply, damages the streets, builds and increases the transmission of infectious diseases. In this paper we propose a new algorithm that allows the detection and automatic localization of burst in water distribution systems. As for detection, the novelty is to use the wavelet correlation criterion to compute the statistical decision and compare it with a detection threshold. The novelty in the localization is to use the statistical operator cross-correlation. The algorithm was implemented in Octave and was validated with 32 signals acquired in the laboratory in a 26.7 m long steel pipe. In 16 signals burst were triggered which were detected under a false positive probability of 2 %. No false positives were present on the 16 signals where only noise was present. | es_ES |
dc.language | Español | es_ES |
dc.publisher | Universitat Politècnica de València | es_ES |
dc.relation.ispartof | Revista Iberoamericana de Automática e Informática industrial | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Singularity | es_ES |
dc.subject | Wavelet transform | es_ES |
dc.subject | Detection | es_ES |
dc.subject | Estimation | es_ES |
dc.subject | Singularidad | es_ES |
dc.subject | Transformada wavelet | es_ES |
dc.subject | Detección | es_ES |
dc.subject | Estimación | es_ES |
dc.title | Detección y Localización de Ruptura Súbita mediante Transformada Wavelet Discreta y Correlación Cruzada | es_ES |
dc.title.alternative | Burst Detection and Localization using Discrete Wavelet Transform and Cross-Correlation | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.4995/riai.2017.8738 | |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | Trutié-Carrero, E.; Valdés-Santiago, D.; León-Mecías, Á.; Ramírez-Beltrán, J. (2018). Detección y Localización de Ruptura Súbita mediante Transformada Wavelet Discreta y Correlación Cruzada. Revista Iberoamericana de Automática e Informática industrial. 15(2):211-216. https://doi.org/10.4995/riai.2017.8738 | es_ES |
dc.description.accrualMethod | OJS | es_ES |
dc.relation.publisherversion | https://doi.org/10.4995/riai.2017.8738 | es_ES |
dc.description.upvformatpinicio | 211 | es_ES |
dc.description.upvformatpfin | 216 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 15 | es_ES |
dc.description.issue | 2 | es_ES |
dc.identifier.eissn | 1697-7920 | |
dc.relation.pasarela | OJS\8738 | es_ES |
dc.description.references | Cedeño, A., Trujillo, R., 2013. Estudio comparativo de técnicas de reducción de ruido en se-ales industriales mediante transformada wavelet discreta y selección adaptativa del umbral. Revista Iberoamericana de Automática e Informática Industrial RIAI 10, 143-148. https://doi.org/10.1016/j.riai.2013.03.003 | es_ES |
dc.description.references | Donoho, D. L., Johnstone, J. M., 1994. Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425-455. https://doi.org/10.1093/biomet/81.3.425 | es_ES |
dc.description.references | Eaton, J. W., Bateman, D., Hauberg, S., Wehbring, R., 2014. GNU Octave version 3.8.1 manual: a high-level interactive language for numerical computations. CreateSpace Independent Publishing Platform. | es_ES |
dc.description.references | Ebacher, G., Besner, M.-C., Prévost, M., Allard, D., 2010. Negative pressure events in water distribution systems: Public health risk assessment based on transient analysis outputs. In: Water Distribution Systems Analysis 2010. pp. 471-483. | es_ES |
dc.description.references | Grinstead, C. M., Snell, J. L., 1997. Introduction to Probability. American Mathematical Society. | es_ES |
dc.description.references | Luo, Jun; Liu, G. H. Z., 2016. Damage detection for shear structures based on wavelet spectral transmissibility matrices under nonstationary stochastic excitation. Structural Control and Health Monitoring. https://doi.org/10.1002/stc.1862 | es_ES |
dc.description.references | Mallat, S., 1999. A Wavelet Tour of Signal Processing. Academic Press. Martini, A., Troncossi, M., Rivola, A., 2013. Vibration monitoring as a tool for leak detection in water distribution networks. In: International Conference Surveillance 7. | es_ES |
dc.description.references | Meniconi, S., Brunone, B., Ferrante, M., Capponi, C., Pedroni, M., Zaghini, M., Leoni, F., 2014. Transmission Main Survey by Transient Tests: The Case of Villanova Plan in Mantova (I). Procedia Engineering 89. https://doi.org/10.1016/j.proeng.2014.11.454 | es_ES |
dc.description.references | Mounce, Stephen R.; Mounce, R. B. B. J. B., 03 2012. Identifying sampling interval for event detection in water distribution networks. Journal of Water Resources Planning and Management 138. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000170 | es_ES |
dc.description.references | Oppenheim, A. V., Schafer, R. W., 2010. Discrete-Time Signal Processing. Prentice Hall. | es_ES |
dc.description.references | Proakis, J. G., Manolakis, D. G., 2006. Digital signal processing: principles, algorithms, and applications. Prentice-Hall. | es_ES |
dc.description.references | Rathnayaka, S., Shannon, B., Rajeev, P., Kodikara, J., 2016. Monitoring of pressure transients in water supply networks. Water Resources Management 30 (2), 471-485. https://doi.org/10.1007/s11269-015-1172-y | es_ES |
dc.description.references | Srirangarajan, S., Allen, M., Preis, A., 2013. Wavelet-based burst event detection and localization in water distribution systems. Journal of Signal Processing Systems 72, 1-16. https://doi.org/10.1007/s11265-012-0690-6 | es_ES |
dc.description.references | Srirangarajan, S., Iqbal, M., Lim, H. B., Allen, M., Preis, A., Whittle, A. J., 2011. Water main burst event detection and localization. In: Water Distribution Systems Analysis 2010. Tucson, Arizona, United States. https://doi.org/10.1061/41203(425)119 | es_ES |
dc.description.references | Ye, G., Fenner, R. A., 2011. Kalman filtering of hydraulic measurements for burst detection in water distribution systems. Journal of Pipeline Systems Engineering and Practice 2, 14-22. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000070 | es_ES |
dc.description.references | Ye, G., Fenner, R. A., 2014. Study of burst alarming and data sampling frequency in water distribution networks. Journal ofWater Resources Planning and Management 140, 06014001-1-06014001-7. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000394 | es_ES |
dc.description.references | Zadkarami, M., Shahbazian, M., Salahshoor, K., 2017. Pipeline leak diagnosis based on wavelet and statistical features using dempster-shafer classifier fusion technique. Process Safety and Environmental Protection 105, 156-163. https://doi.org/10.1016/j.psep.2016.11.002 | es_ES |
dc.description.references | Zan, T. T. T., Wong, K.-J., Lim, H. B., Whittle, A., 2011. A frequency domain burst detection technique for water distribution systems. In: 2011 IEEE Sensors Proceedings. pp. 1870-1873. https://doi.org/10.1109/ICSENS.2011.6127324 | es_ES |