Abstract
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.
Highlights
)LJXUHHill chart for Francis turbine: regions of different types of cavitation 1: leading edge cavitation suction side, 2: leading edge cavitation pressure side 3: interblade cavitation, 4: ring swirl cavitation Here a new approach is applied
The deterioration the ultrasonic signals due to the various cavitation effects is exploited in a statistical way. 0HDVXUHPHQWV Measurements were carried out at three objects by mounting the acoustic sensors in a clamp-on fashion from the outside of the fluidized section of the installations (Müller [3], Gruber et al [4]): - sphere in a vertical pipe of perspex at the hydraulic laboratory at the HSLU (Figure 3) - different profiles in the cavitation channel of the EPFL-LMH laboratory (Figure 4) - Francis model test turbine at the test rig at EPFL-LMH (Figure 5) Figures 3 – 5 show the three installations tested and, Figures 6 - 8 the corresponding acoustic path locations
)LJXUHHistogram of 100 measurements of amplitude: left undisturbed, right disturbed 3.2 Statistical signal parameters for correlation function An interesting function to examine is the correlation of each incoming signal with a reference signal that corresponds to a signal obtained in undisturbed conditions
Summary
)LJXUHHill chart for Francis turbine: regions of different types of cavitation 1: leading edge cavitation suction side, 2: leading edge cavitation pressure side 3: interblade cavitation, 4: ring swirl cavitation Here a new approach is applied. )LJXUHsamples of correlation functions: upper left undisturbed signal, other signals: different degree of disturbance 3.3 Selection of important statistical parameters The selection of the most important statistical parameters for the neural network classifiers was done in the following way: - To begin with simple combinations and increase complexity if needed - For classifiers with only two inputs the selection has been done manually by looking to simple conditions including thresholds by which most of the training data can be separated. In case of a decision tree classifier, the inputs were chosen as for the neural network or they were selected by an automated search.
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