Research On Vibration Analysis And Fault Diagnosis Method Of MW Grade Wind Turbine Key Components

Wind power is an important and clean energy, which is developed vigorously in china.But many problems of fault diagnosis and dynamic monitoring has been exposed. Becauseof the large-scale wind turbine, the interference, weak signal characteristics, the role ofturbulence, and fluid-structure interaction, traditional methods is restricted and insufficientto complete the monitoring of wind turbine failure.The dynamic analysis of the gearbox concentrated in the planetary gear train usually.The planetary gear train is also an important component of the wind turbine gearbox. Inthis paper, the dynamic model of the planetary gear combined with ordinary gear train andshafting is constructed. Vibration excitation and vibration characteristics of the windturbine gearbox are introduced, and mathematical expression based on the dynamic modelis built. The subspace iteration method is used to solve the system modal, then real caseprove the feasibility of algorithm. The vibration characteristics of major components in thestable operation have typical feature and limitations. When some parts have internal failure,the form of vibration signal amplitude and spectral components will change. Differentdefects and malfunctions correspond to different modes of vibration. Therefore, vibrationsignal can reflect the operating state of gearbox objectively. Traditional fault diagnosismethods lack rigorous theoretical reasoning and proving data. This paper presents thatneural network approach is used to study wind turbine gearbox fault diagnosis. It wasconducted that specific results of bearing fault diagnosis can be achieved.The application of acoustic emission(AE) in wind turbine component fault diagnosisis proposed. The AE signal is different from the vibration signal. Therefore, it enrich thefault diagnosis methods and characteristics identification methods. AE signal and vibrationsignal are applied in different components in pertinences respectively. Using theiradvantages can push forward the development of wind turbine monitoring and faultdiagnosis. Nowadays, fault monitoring for wind turbine blades is rare. This paper presents theflow field simulation based on aerodynamic theory to study the turbulence of the windfield model.It is arrived that the force at root and torque around Y-axis are large, and forceand moment of Z-axis is little. Fluid-solid coupling model of Blade and flow field andblade flutter model are established. Blade flutter stability criterion is studied, and judgmentformula of leaves flutter stiffness damping and aerodynamic damping are obtained.Through the calculating of data of real blades, the presence and the frequency range ofblade flutter are confirmed, which lay the foundation for fault diagnosis of the wind turbineblades.Extraction early crack characteristics with strong noise and identification differentkinds of cracks in wind turbine blades are important in wind turbine fault diagnosis.Experimental platform has been constructed to test a wind turbine blade that is made ofcomposite material, and to collect the AE signal of propagation crack and initiation crack.Wavelet scalogram was used to extract crack AE signal characteristics and identify thepropagation crack and initiation crack for its superior time-frequency analysis feature. Theresults reveal that the wavelet scalogram can extract nonlinear, non-stationary faultfeatures effectively, which is better than the wavelet analysis. It is also indicated thecriterion of propagation crack and initiation crack. Finally, it has been established the newcrack identification method of wind turbine blades based on AE and wavelet scalogram.