Characteristic Analysis of Gas Turbine Blade Conditions and Casing Vibrations for Fault Early Warning

doi:10.3901/JME.2025.23.182

Abstract

Abstract: Blade failure is one of the most serious failures of the gas turbine. Real-time monitoring and early warning of blade failure is an effective method to improve the operational reliability. Based on the casing vibration signals, this study explores the influence of blade conditions on the pressure and vibration of the casing of gas turbine, in order to solve the difficult problem of early warning and diagnosis of blade failures. The dynamic pressure in the wake flow caused by the normal and fault conditions of the rotating blades is firstly simulated based on the Fourier series superposition, and the transmission characteristics of the dynamic pressure in the casing are analyzed. Secondly, a simplified finite element model of the casing-stator-blade based on plane beam element is proposed and established, and the natural frequency and mode shapes of the casing are solved, and the vibration characteristics and response law of the casing under normal and fault wake pressure are analyzed at the same time. Based on the above theoretical, the blade early warning indicators and diagnostic process applicable to the failure of gas turbine are proposed. Finally, it is verified by two actual industrial blade failure cases of gas turbines. The results show that the wake pressure generated by the rotating blade mainly consists of multi-order high-frequency blade passing frequency components and rotational frequency components, and the amplitude of each order decreases with the increase of the order; blade failure causes the change of blade passing frequency amplitude in the casing vibration signal, and the more significant is the sidebands on both sides of the frequency with the rotational frequency as the interval, and these features are crucial for the identification of blade failure, which is the basis for the establishment of blade fault warningindicators. The results provide theoretical guidance for improving the operation reliability of the gas turbine.

Key words: gas turbine, blade, vibration, pressure, fault warning

CLC Number:

TK478

XIAO Yuan, FENG Kun, ZHANG Peng. Characteristic Analysis of Gas Turbine Blade Conditions and Casing Vibrations for Fault Early Warning[J]. Journal of Mechanical Engineering, 2025, 61(23): 182-192.

References

[1] CHOWDHURY T S,MOHSIN F T,TONNI M M,et al. A critical review on gas turbine cooling performance and failure analysis of turbine blades[J]. International Journal of Thermofluids,2023(18):100329.
[2] ZABIHU A, AGHDASI F, ELLOUZI C, et al. Non-Contact wind turbine blade crack detection using laser doppler vibrometers[J]. Energies,2024,17(9):2165.
[3] YUAN K,ZHU W D. Identification of modal parameters of a model turbine blade with a curved surface under random excitation with a three-dimensional continuously scanning laser doppler vibrometer system[J].Measurement,2023(214):112759.
[4] ORAVEC M, PAVOL L, MIROSLAV S, et al. Low-frequency magnetic fields in diagnostics of low-speed electrical and mechanical systems[J]. Sustainability,2021,13(16):9197.
[5] CHEN Z,SHENG H,XIA Y,et al. A comprehensive re-view on blade tip timing-based health monitoring:Status and future[J]. Mechanical Systems and Signal Processing,2021,149:107330.
[6] 张玉贵,段发阶,方志强,等. 基于叶尖定时的非接触式旋转叶片异步振动分析[J]. 机械工程学报,2008, 44(7):147-150. ZHANG Yugui,DUAN Fajie,FANG Zhiqiang,et al. Analysis of non-contact asynchronous vibration of rotating blades based on tip-timing[J]. Journal of Mechanical Engineering,2008,44(7):147-150.
[7] MATHIOUDAKIS K,LOUKIS E,PAPAILIOU K D. Casing vibration and gas turbine operating conditions[C]//ASME. J. Eng. Gas Turbines Power,1990,112(4):478-485.
[8] FORBES G L,RANDALL R B,Estimation of turbine blade natural frequencies from casing pressure and vibration measurements[J]. Mechanical Systems and Signal Processing,2013,36(2):549-561.
[9] FORBES G L,RANDALL R B. Resonance phenomena of an elastic ring under a moving load[J]. Journal of Sound and Vibration,2008,318(4-5):991-1004.
[10] ALSHROOF O N,FORBES G L,SAWALHI N,et al. Computational fluid dynamic analysis of a vibrating turbine blade[J]. International Journal of Rotating Machinery,2012,2012(1):1-15.
[11] RAO A,DUTTA B K. Vibration analysis for detecting failure of compressor blade[J]. Engineering Failure Analysis,2012,25:211-218.
[12] LIM M H,LEONG M S. Reconstruction of vital blade signal from unsteady casing vibration[J]. Advances in Mechanical Engineering,2014,6:146983.
[13] 党伟. 燃气轮机叶片断裂故障诊断方法研究[D]. 北京:北京化工大学,2020. DANG Wei. Research on fault diagnosis method of gas turbine blade fracture[D]. Beijing:Beijing University of Chemical Technology,2020.
[14] 王钟. 燃气轮机叶片断裂故障机理及识别方法研究[D]. 北京:北京化工大学,2022. WANG Zhong. Research on mechanism and identification method of gas turbine blade fracture fault[D]. Beijing:Beijing University of Chemical Technology,2022.
[15] FENG K,XIAO Y,LI Z,et al. Gas turbine blade fracturing fault diagnosis based on broadband casing vibration[J]. Measurement,2023,214:112718.
[16] HORLOCK J H. Axial flow turbines:fluid mechanics and thermodynamics[M]. Krieger Publishing Company:Malabar,FL,USA,1985.
[17] ZHOU L,ZHANG J, LUO X,et al. An aerodynamic ROM of the blade subjected to wake based on Fourier method for flow[J]. International Journal for Numerical Methods in Fluids,2019,89(4-5):162-179.
[18] CLOUGH R W,PENZIEN J. Dynamic of structures[M]. New York:McGraw-Hill 1975.
[19] SAFFARI H, TABATABAEI R, MANSOURI S H. Vibration analysis of circular arch element using curvature[J]. Shock and Vibration,2008,15(5):481-492.
[20] SOEDEL W. Vibrations of shells and plates[M]. London:CRC Press,2004.
[21] YUAN P,DONG Y. High-efficient decoupling method for coupling systems with multiple subdomains and time steps[J]. Mechanical Systems and Signal Processing, 2022,163:108159.
[22] DADON I,KOREN N,KLEIN R,et al. A realistic dynamic model for gear fault diagnosis[J]. Engineering Failure Analysis,2018,84:77-100.
[23] SHAO H,JIANG H,ZHAO H,et al. A novel deep auto-encoder feature learning method for rotating machinery fault diagnosis[J]. Mechanical Systems and Signal Processing,2017,95:187-204.
[24] FENG K,XIAO Y,LI Z,et al. A fusion autoencoder model and piecewise anomaly index for aero-engine fault diagnosis[J]. Applied Intelligence, 2024, 54(20):10148-10160.