Burst Detection and Localization using Discrete Wavelet Transform and Cross-Correlation

[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.[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.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.2017.8738OJS211216152Cedeño, 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.2013.03.003Donoho, D. L., Johnstone, J. M., 1994. Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425-455. https://doi.org/10.1093/biomet/81.3.425Eaton, 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.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.Grinstead, C. M., Snell, J. L., 1997. Introduction to Probability. American Mathematical Society.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.1862Mallat, 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.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.2014.11.454Mounce, 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.1943-5452.0000170Oppenheim, A. V., Schafer, R. W., 2010. Discrete-Time Signal Processing. Prentice Hall.Proakis, J. G., Manolakis, D. G., 2006. Digital signal processing: principles, algorithms, and applications. Prentice-Hall.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-ySrirangarajan, 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/s11265-012-0690-6Srirangarajan, 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)119Ye, 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.1949-1204.0000070Ye, 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.1943-5452.0000394Zadkarami, 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.2016.11.002Zan, 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.2011.612732

A High-Resolution Dyadic Transform for Non-Stationary Signal Analysis

This article shows a new Te-transform and its periodogram for applications that mainly exhibit stochastic behavior with a signal-to-noise ratio lower than −30 dB. The Te-transform is a dyadic transform that combines the properties of the dyadic Wavelet transform and the Fourier transform. This paper also provides another contribution, a corollary on the energy relationship between the untransformed signal and the transformed one using the Te-transform. This transform is compared with other methods used for the analysis in the frequency domain, reported in literature. To perform the validation, the authors created two synthetic scenarios: a noise-free signal scenario and another signal scenario with a signal-to-noise ratio equal to −69 dB. The results show that the Te-transform improves the sensitivity in the frequency spectrum with respect to previously reported methods

A High-Resolution Dyadic Transform for Non-Stationary Signal Analysis

This article shows a new Te-transform and its periodogram for applications that mainly exhibit stochastic behavior with a signal-to-noise ratio lower than −30 dB. The Te-transform is a dyadic transform that combines the properties of the dyadic Wavelet transform and the Fourier transform. This paper also provides another contribution, a corollary on the energy relationship between the untransformed signal and the transformed one using the Te-transform. This transform is compared with other methods used for the analysis in the frequency domain, reported in literature. To perform the validation, the authors created two synthetic scenarios: a noise-free signal scenario and another signal scenario with a signal-to-noise ratio equal to −69 dB. The results show that the Te-transform improves the sensitivity in the frequency spectrum with respect to previously reported methods

Diseño de Mezclador Activo con baja figura de ruido para aplicaciones en la banda ISM de 2.4 GHz

In this paper the design of an active gate mixer is proposed to work at the center frequency of 2.4 GHz in the ISM band, in addition to its implementation to validate the results obtained by the simulation process. The substrate used for the circuit implementation is FR-4 with dielectric permittivity 4.5 and loss tangent of 0.022. The tools used for this design were the AWR Microwave Office v.9, APPCAD and building the CAM 350 software. The circuit size is 8 cm X 5.6 cm. Built circuit for converting a gain of 6.7 dB is achieved a noise figure of 3.9 dB.En el presente trabajo se propone el diseño de un mezclador activo por puerta para que trabaje a la frecuencia central de la banda ISM de 2.4 GHz, además de la implementación del mismo para validar los resultados obtenidos mediante el proceso de simulación. El sustrato empleado para la implementación del circuito es FR-4 con permitividad dieléctrica de 4.5 y tangente de pérdida de 0.022. Las herramientas utilizadas para este diseño fueron el programa AWR Microwave Office v.9, APPCAD y para la construcción el CAM 350. El tamaño del circuito es de 8 cm X 5.6 cm. Para el prototipo implementado se logró una ganancia de conversión de 6.7 dB una figura de ruido de 3.9 dB

Detection of Background Water Leaks Using a High-Resolution Dyadic Transform

This article solves the problem of detecting water leaks with a minimum size of down to 1 mm in diameter. Two new mathematical tools are used to solve this problem: the first one is the Te cross-spectral density and the second is Te coherence. These mathematical tools provide the possibility of discriminating spurious frequency components, making use of the property of multi-sensitivity. This advantage makes it possible to maximize the sensitivity of the frequency spectrum. The wavelet function used was Daubechies 45, because it provides an attenuation of 150 dB in the rejection band. The tools were validated with two scenarios. For the first scenario, a synthetic signal was analyzed. In the second scenario, two types of background leakage were analyzed: the first one has a diameter of 1 mm with a signal-to-noise ratio of 2.82 dB and flow rate of 33.7 mL/s, and the second one has a diameter of 4 mm with a signal-to-noise ratio of 9.73 dB with a flow rate of 125.0 mL/s. The results reported in this paper show that both the Te cross-spectral density and Te coherence are higher than those reported in scientific literature

Design of a Fuzzy Logic Evaluation to Determine the Ergonomic Risk Level of Manual Material Handling Tasks

In this work, we propose a fuzzy inference as a decision support system built in the MATLAB Fuzzy Logic Designer for evaluating manual material handling risk conditions. The input variables for the fuzzy decision were: (1) the total time duration of the manual material handling in one shift of 450 min, with 3 h considered the maximal exposition time; (2) 25 kg as a maximal mass reference which should never be exceeded; (3) the repetitiveness of the manual material handling task through the shift considering as the maximal frequency of four lifts per min. Results of 135 earlier direct ergonomic evaluations made using the method proposed by the ISO 11228-1 were used as validator results, and called “expected results”. The experimentation intended to simulate an ergonomic evaluation in different boundary conditions of work and verify if the fuzzy interface could correctly replicate the results of the ergonomic evaluations. As validation, the list with the 135 expected results was compared against the evaluation made by the fuzzy logic interface, called “Work_Conditions”. From the comparison, only three evaluations (0.02%) differed with respect to the expected results. Consequently, it is concluded that the fuzzy interface can be used as a tool for automating the determination of manual material handling ergonomic risk levels, with great precision