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The detection of cavitation in hydraulic machines by use of ultrasonic signal analysis
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 Title & Authors
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.;
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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.
ultrasonic signals;cavitation;turbine;neural network;decision tree;
 Cited by
Avellan F 2004 Introduction to cavitation in hydraulic machinery (The 6th International Conference on Hydraulic Machinery and Hydrodynamics Timisoara, Romania)

Escaler X, Egusquiza E, Farhat M, Avellan F, Coussirat M 2006 Detecton of cavitation in hydraulic turbines (Mechanical Systems and Signal Processing 20) p.983-1007

Müller C 2008 Untersuchung der Kavitation mit Ultraschall an zwei Prufstrecken (Bachelor Diplomarbeit HSLU Luzern)

Gruber P, Roos D, Müller C, Staubli T 2011 Detection of damaging cavitation states by means of ultrasonic signal parameter patterns (WIMRC 3rd International Cavitation Forum, July 2011, Warwick, England)

Hassoun M H 1995 Fundamentals of artificial neural networks (MIT Press)

Press W, Teukolski S A, Vetterling W T, Flannery B P 1986 Numerical Recipes(Cambridge University Press)

Etterlin M 2012 Klassifizierung von Wasserzuständen mithilfe von Ultraschallsignalen und neuronalen Netzen (Industrieprojekt, HSLU Luzern)

Lerch T 2013 Klassifizierung von Kavitationszustanden mithilfe von Ultraschallsignalen (Industrieprojekt, HSLU Luzern)

Gruber P, Odermatt P, Etterlin M, Lerch T, Farhat M 2013 Cavitation Detection via Ultrasonic Signal Characteristics (IAHR, 5th International Workshop on Cavitation and Dynamic Problems in Hydraulic Machinery, Lausanne, Switzerland)

Gruber P, Odermatt P, Etterlin M, Lerch T 2013 The detection of cavitation in hydraulic machines by use of ultrasonic signal analysis (CTI-Report)

Breiman L, Friedman J H, Olshen R A, Stone C J 1984 Classification and regression trees (Chapman and Hall)

Breiman L 1996 Technical Note: Some Properties of Splitting Criteria (Machine Learning) p. 24, 41-47

Hand D, Mannila H, Smyth P 2001: Data Mining (MIT Press)

Frei M 2013 Klassifizierung von Kavitationszuständen mithilfe von Ultraschallsignalen und regelbasierten Methoden (Industrieprojekt, HSLU Luzern)