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

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 a Francis model test turbine both 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 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.


Published in:
Proceedings of the 27th IAHR Symposium on Hydraulic Machinery and Systems, 1, 1-10
Presented at:
27th IAHR Symposium on Hydraulic Machinery and Systems, Montréal, Canada, September 22-26, 2014
Year:
2014
Publisher:
International Association For Hydraulic Research
Laboratories:




 Record created 2018-05-21, last modified 2019-08-12

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