Vibration Identification of Engine Blade Monitoring Based on Enhanced Sparse Decomposition
With regard to the limit of blade signal monitoring by the monitoring method of sticking strain gauge on the blade surface and difficulty in signal transmission,a vibration identification method based on enhanced sparse decomposition(ESD)for engine blade monitoring was designed,and vibration signal identification under non-undersampled and undersampled conditions was studied.The results show that under non-under-sampled vibration,compared with the comparison matrix eigenspace decomposition(MUSIC)method and the nonlinear least square fitting(NLS)method,the enhanced sparse decomposition can fully filter out the influence of other frequency components.Under under-sampled vibration,MUSIC method and NLS method are far from satisfaction,but the discrimination accuracy of ESD is still very high.Under under-sampled synchronous vibration,both MUSIC method and NLS method are difficult to identify the frequency components,while ESD can still realize the discrimination of each component.The simulation experiment verifies that the ESD method can accurately identify the vibration signals of blades,which plays a certain theoretical supporting significance for the subsequent performance optimization.
万方数据
维普
