The detection of cavitation in hydraulic machines by use of ultrasonic signal analysis

Gruber, P.; Farhat, M.; Odermatt, P.; Etterlin, M.; Lerch, T.; Frei, M.

doi:http://dx.doi.org/10.5293/IJFMS.2015.8.4.264

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자료유형: 학술저널

저자정보: Gruber, P. Farhat, M. Odermatt, P. Etterlin, M. Lerch, T. Frei, M.

저널정보: 한국유체기계학회 International Journal of Fluid Machinery and Systems International Journal of Fluid Machinery and Systems Vol.8 No.4

발행연도: 2015.12

수록면: 264 - 273 (10page)

DOI: http://dx.doi.org/10.5293/IJFMS.2015.8.4.264

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This presentation describes an experimental approach for the detection of cavitation in hydraulic machines by use of ultrasonic signal analysis. Instead of using the high frequency pulses (typically 1MHz) only for transit time measurement different other signal characteristics are extracted from the individual signals and its correlation function with reference signals in order to gain knowledge of the water conditions. As the pulse repetition rate is high (typically 100Hz), statistical parameters can be extracted of the signals. The idea is to find patterns in the parameters by a classifier that can distinguish between the different water states. This classification scheme has been applied to different cavitation sections: a sphere in a water flow in circular tube at the HSLU in Lucerne, a NACA profile in a cavitation tunnel and two Francis model test turbines all at LMH in Lausanne. From the signal raw data several statistical parameters in the time and frequency domain as well as from the correlation function with reference signals have been determined. As classifiers two methods were used: neural feed forward networks and decision trees. For both classification methods realizations with lowest complexity as possible are of special interest. It is shown that two to three signal characteristics, two from the signal itself and one from the correlation function are in many cases sufficient for the detection capability. The final goal is to combine these results with operating point, vibration, acoustic emission and dynamic pressure information such that a distinction between dangerous and not dangerous cavitation is possible.

#neural network #ultrasonic signals #decision tree #turbine #cavitation

참고문헌 (14)

참고문헌 신청

[학술대회논문] - Avellan F / 2004 / Introduction to cavitation in hydraulic machinery / The 6th International Conference on Hydraulic Machinery and Hydrodynamics

[학술지(정기간행물)] - Escaler X / 2006 / Detecton of cavitation in hydraulic turbines / Mechanical Systems and Signal Processing 20 : 983 ~ 1007

[단행본] - Müller C / 2008 / Untersuchung der Kavitation mit Ultraschall an zwei Prüfstrecken / Bachelor Diplomarbeit HSLU Luzern

[학술대회논문] - Gruber P / 2011 / Detection of damaging cavitation states by means of ultrasonic signal parameter patterns / WIMRC 3rd International Cavitation Forum

[단행본] - Hassoun M H / 1995 / Fundamentals of artificial neural networks / MIT Press

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