Skip to content
Licensed Unlicensed Requires Authentication Published by Oldenbourg Wissenschaftsverlag September 26, 2019

Analytic wavelet packets for robust ultrasonic flow measurement

Analytische Wavelet-Pakete zur robusten Ultraschall-Durchflussmessung
Matthias Bächle

Matthias Bächle obtained his bachelor’s and master’s degree in Electrical Engineering/Information Technology at the Karlsruhe Institute of Technology, Germany in 2014 and 2016, respectively. He is currently working as a research associate at the Institute of Industrial Information Technology (IIIT) at the Karlsruhe Institute of Technology, Germany, where he is pursuing his Ph. D. degree. His current research interests include signal processing techniques and their application to flow measurement using transit-time of ultrasonic pulses.

ORCID logo EMAIL logo
, Daniel Alexander Schwär

Daniel Schwär received his bachelor’s degree in Electrical Engineering/Information Technology at the Karlsruhe Institute of Technology (KIT), Germany in 2016. He is currently doing his master’s degree also in Electrical Engineering/Information Technology at the KIT with the focus on Signal processing. His master thesis was about flow measurement with wavelet transform at the Institute of Industrial Information Technology (IIIT).

ORCID logo
and Fernando Puente León

Fernando Puente León is a Professor with the Department of Electrical Engineering and Information Technology at Karlsruhe Institute of Technology, Germany, where he heads the Institute of Industrial Information Technology (IIIT). From 2001 to 2002, he was with DS2, Valencia, Spain. From 2002 to 2003, he was a postdoctoral Research Associate with the Institut für Mess- und Regelungstechnik, University of Karlsruhe. From 2003 to 2008, he was a Professor with the Department of Electrical Engineering and Information Technology, Technische Universität München, Germany. His research interests include image processing, automated visual inspection, information fusion, measurement technology, and pattern recognition.

From the journal tm - Technisches Messen

Abstract

A key element in robust transit-time ultrasonic flow measurement is the accurate estimation of the transit-time difference. Conventional methods, such as cross-correlation or the estimation in the phase domain, are limited in their robustness against signal distortions, interfering signals or noise. In this work, we present a novel method to estimate the transit-time difference through the fusion of selected analytic wavelet packet coefficients. The combination of the complex coefficients, which represent a projection of the signal on analytic wavelets, with a configurable time-frequency resolution allows a sub-sample estimation at the frequency of interest. After giving an introduction into the fundamentals of analytic wavelet packets based on multi-scale filtering, we introduce two features that correlate strongly with the transit-time difference. The selection and fusion of these features is done by using correlation coefficients with a calibration set and principal component analysis. Finally, using a clamp-on flow measurement system, the robustness against temperature variation and measurement noise is shown and compared with conventional methods.

Zusammenfassung

Ein wichtiger Baustein zur robusten Ultraschall-Durchflussmessung, basierend auf dem Laufzeitdifferenzprinzip, ist die genaue Schätzung der Laufzeitdifferenz. Konventionelle Methoden, wie die Kreuzkorrelation oder die Schätzung im Phasenbereich, sind in ihrer Robustheit gegenüber Signalverzerrungen, Störsignalen oder Rauschen beschränkt. In dieser Arbeit präsentieren wir eine neue Methode zur Schätzung der Laufzeitdifferenz durch die Auswahl und Fusion von Koeffizienten aus analytischen Wavelet-Paketen. Die Kombination der komplexen Koeffizienten, die eine Projektion des Signals auf die analytischen Wavelets darstellen, mit einer einstellbaren Zeit-Frequenz-Auflösung erlaubt eine sub-sample-genaue Schätzung bei der relevanten Frequenz. Nach einer Einführung in die Grundlagen der auf Multiraten-Filterbänken basierenden analytischen Wavelet-Pakete führen wir zwei Merkmale ein, die stark mit der Laufzeitdifferenz korrelieren. Die Auswahl und Fusion dieser Merkmale wird mithilfe von Korrelationskoeffizienten an Kalibrierdaten und der Hauptkomponentenanalyse durchgeführt. Abschließend wird die Robustheit gegenüber Temperaturänderungen und Messrauschen an einem Clamp-on-Durchflussmesssystem gezeigt und mit den konventionellen Methoden verglichen.

About the authors

Matthias Bächle

Matthias Bächle obtained his bachelor’s and master’s degree in Electrical Engineering/Information Technology at the Karlsruhe Institute of Technology, Germany in 2014 and 2016, respectively. He is currently working as a research associate at the Institute of Industrial Information Technology (IIIT) at the Karlsruhe Institute of Technology, Germany, where he is pursuing his Ph. D. degree. His current research interests include signal processing techniques and their application to flow measurement using transit-time of ultrasonic pulses.

Daniel Alexander Schwär

Daniel Schwär received his bachelor’s degree in Electrical Engineering/Information Technology at the Karlsruhe Institute of Technology (KIT), Germany in 2016. He is currently doing his master’s degree also in Electrical Engineering/Information Technology at the KIT with the focus on Signal processing. His master thesis was about flow measurement with wavelet transform at the Institute of Industrial Information Technology (IIIT).

Fernando Puente León

Fernando Puente León is a Professor with the Department of Electrical Engineering and Information Technology at Karlsruhe Institute of Technology, Germany, where he heads the Institute of Industrial Information Technology (IIIT). From 2001 to 2002, he was with DS2, Valencia, Spain. From 2002 to 2003, he was a postdoctoral Research Associate with the Institut für Mess- und Regelungstechnik, University of Karlsruhe. From 2003 to 2008, he was a Professor with the Department of Electrical Engineering and Information Technology, Technische Universität München, Germany. His research interests include image processing, automated visual inspection, information fusion, measurement technology, and pattern recognition.

References

1. G. Andria, F. Attivissimo, and N. Giaquinto. Digital signal processing techniques for accurate ultrasonic sensor measurement. Measurement, 30 (2): 105–114, 2001.10.1016/S0263-2241(00)00059-2Search in Google Scholar

2. J. Beyerer, M. Richter, and M. Nagel. Pattern Recognition: Introduction, Features, Classifiers and Principles. Walter de Gruyter GmbH & Co KG, 2017.Search in Google Scholar

3. I. Daubechies. Ten lectures on wavelets. Regional conference series in applied mathematics. Society for Industrial and Applied Mathematics, Philadelphia, Pa., 1992.Search in Google Scholar

4. C. Guetbi, D. Kouame, A. Ouahabi, and J. P. Chemla. Methods based on wavelets for time delay estimation of ultrasound signals. In 1998 IEEE International Conference on Electronics, Circuits and Systems. Surfing the Waves of Science and Technology, volume 3, pages 113–116, 1998.Search in Google Scholar

5. U. Hempel, S. Wöckel, and J. Auge. Ultraschallbasierte informationsgewinnung in der verfahrenstechnik. Chemie Ingenieur Technik, 82 (4): 491–502, 2010.10.1002/cite.200900140Search in Google Scholar

6. M. Hua, W. Hui, and L. Mingwei. High-precision flow measurement for an ultrasonic transit time flowmeter. In 2010 International Conference on Intelligent System Design and Engineering Application, volume 1, pages 823–826, 2010.Search in Google Scholar

7. S. A. Jacobson, P. N. Denbigh, and D. E. H. Naudé. A new method for the demodulation of ultrasonic signals for cross-correlation flowmeters. Ultrasonics, 23 (3): 128–132, 1985.10.1016/0041-624X(85)90061-7Search in Google Scholar

8. N. Kingsbury. Design of q-shift complex wavelets for image processing using frequency domain energy minimization. In Proceedings 2003 International Conference on Image Processing, volume 1, pages 1013–1016, 2003.Search in Google Scholar

9. W. T. Kuang and A. S. Morris. Using short-time fourier transform and wavelet packet filter banks for improved frequency measurement in a doppler robot tracking system. IEEE Transactions on Instrumentation and Measurement, 51 (3): 440–444, 2002.10.1109/TIM.2002.1017713Search in Google Scholar

10. M. Kupnik, E. Krasser, and M. Gröschl. Absolute transit-time detection for ultrasonic gas flowmeters based on time and phase domain characteristics. In 2007 IEEE Ultrasonics Symposium Proceedings, pages 142–145, 2007.Search in Google Scholar

11. L. C. Lynnworth and Y. Liu. Ultrasonic flowmeters: Half-century progress report, 1955–2005. Ultrasonics, 44 (Suppl. 1): e1371–e1378, 2006.10.1016/j.ultras.2006.05.046Search in Google Scholar

12. E. Mandard, D. Kouame, R. Battault, J.-P. Remenieras, and F. Patat. Methodology for developing a high-precision ultrasound flow meter and fluid velocity profile reconstruction. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 55 (1): 161–172, 2008.10.1109/TUFFC.2008.625Search in Google Scholar

13. V. Matz, M. Kreidl, and R. Smid. Classification of ultrasonic signals. International Journal of Materials, 27(3): 145–155, 2006.Search in Google Scholar

14. N. Michalodimitrakis and T. Laopoulos. On the use of wavelet transform in ultrasonic measurement systems. In IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics, volume 1, pages 589–594, 2001.Search in Google Scholar

15. E. Oruklu, N. Jayakumar, and J. Saniie. Ultrasonic signal compression using wavelet packet decomposition and adaptive thresholding. In 2008 IEEE Ultrasonics Symposium, pages 171–175, 2008.Search in Google Scholar

16. N. Roosnek. Novel digital signal processing techniques for ultrasonic gas flow measurements. Flow Measurement and Instrumentation, 11 (2): 89–99, 2000.10.1016/S0955-5986(00)00008-XSearch in Google Scholar

17. S. J. Rupitsch. Piezoelectric Sensors and Actuators. Springer-Verlag Berlin Heidelberg, Heidelberg, 2018.Search in Google Scholar

18. I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury. The dual-tree complex wavelet transform. IEEE signal processing magazine, 22 (6): 123–151, 2005.10.1109/MSP.2005.1550194Search in Google Scholar

19. G. Serbes, N. Aydin, and H. O. Gulcur. Directional dual-tree complex wavelet packet transform. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 3046–3049, 2013.Search in Google Scholar

20. T. Strunz, A. Wiest, A. Fleury, and T. Fröhlich. Influence of turbulence on ultrasonic flow measurements. In 5th IGHEM Conference, 2004.Search in Google Scholar

21. K. Tawackolian, O. Büker, J. Hogendoorn, and T. Lederer. Investigation of a ten-path ultrasonic flow meter for accurate feedwater measurements. Measurement Science and Technology, 25: 075304, 2014.Search in Google Scholar

22. T. Weickert, C. Benjaminsen, and U. Kiencke. Analytic wavelet packets—combining the dual-tree approach with wavelet packets for signal analysis and filtering. IEEE Transactions on Signal Processing, 57 (2): 493–502, 2009.10.1109/TSP.2008.2007922Search in Google Scholar

23. H. Zhang, C. Guo, and J. Lin. Effects of velocity profiles on measuring accuracy of transit-time ultrasonic flowmeter. Applied Sciences, 9 (8): 1648, 2019.10.3390/app9081648Search in Google Scholar

Received: 2019-06-25
Accepted: 2019-09-03
Published Online: 2019-09-26
Published in Print: 2020-01-28

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 26.11.2022 from frontend.live.degruyter.dgbricks.com/document/doi/10.1515/teme-2019-0093/html
Scroll Up Arrow