4.7 Article

Detection of cavitation in hydraulic turbines

期刊

MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 20, 期 4, 页码 983-1007

出版社

ACADEMIC PRESS LTD ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2004.08.006

关键词

cavitation detection; hydraulic turbines; vibrations; amplitude demodulation

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An experimental investigation has been carried out in order to evaluate the detection of cavitation in actual hydraulic turbines. The methodology is based on the analysis of structural vibrations, acoustic emissions and hydrodynamic pressures measured in the machine. The proposed techniques have been checked in real prototypes suffering from different types of cavitation. In particular, one Kaplan, two Francis and one Pump-Turbine have been investigated in the field. Additionally, one Francis located in a laboratory has also been tested. First, a brief description of the general features of cavitation phenomenon is given as well as of the main types of cavitation occurring in hydraulic turbines. The work presented here is focused on the most important ones which are the leading edge cavitation due to its erosive power, the bubble cavitation because it affects the machine performance and the draft tube swirl that limits the operation stability. Cavitation detection is based on the previous understanding of the cavity dynamics and its location inside the machine. This knowledge has been gained from flow visualisations and measurements in laboratory devices such as a high-speed cavitation tunnel and a reduced scale turbine test rig. The main techniques are the study of the high frequency spectral content of the signals and of their amplitude demodulation for a given frequency band. Moreover, low frequency spectral content can also be used in certain cases. The results obtained for the various types of cavitation found in the selected machines are presented and discussed in detail in the paper. Conclusions are drawn about the best sensor, measuring location, signal processing and analysis for each type of cavitation, which serve to validate and to improve the detection techniques. (c) 2004 Elsevier Ltd. All rights reserved.

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