Abstract
The failure of the hydraulic Inlet guide vane (IGV) system needs to be avoided in the gas turbine compressor, since the IGV system is critical for the function and efficiency of the gas turbine and its fault can even cause the gas turbine to jump off the power grid. This paper investigates the detection of external and internal leakages, whose levels can be represented through corresponding leakage coefficients, at the cylinder in the hydraulic IGV system. Based on the dynamic model, we propose the recursive ridge regression parameter estimation method to detect and isolate different leakage coefficients under varying load. The developed algorithm is verified through experiments in a hydraulic IGV emulator. Based on experimental results, the proposed scheme can estimate leakage coefficients with good performance.
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Recommended by Associate Editor Byeng Dong Youn
Xin Wu received the B.Sc. and M.Sc. degrees from University of Science and Technology, Beijing, China in 2002 and 2005, respectively. He received the Ph.D. degree in Department of Mechanical Engineering at University of Wisconsin — Milwaukee, USA in 2010. Dr. Wu is currently Associate Professor in School of Energy, Power and Mechanical Engineering at North China Electric Power University. His major research interests are mechanical systems fault diagnostics, prognostics and control.
Yibing Liu received the Ph.D. degree in School of Mechanical Engineering at Leibniz Universität Hannover, Germany in 1999. Dr. Liu is currently Professor in School of Energy, Power and Mechanical Engineering at North China Electric Power University. His major research interests are mechanical systems dynamic analysis, control and fault diagnostics.
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Wu, X., Liu, Y. Leakage detection for hydraulic IGV system in gas turbine compressor with recursive ridge regression estimation. J Mech Sci Technol 31, 4551–4556 (2017). https://doi.org/10.1007/s12206-017-0901-y
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DOI: https://doi.org/10.1007/s12206-017-0901-y