Wheel Out-of Round Diagnosis Based on Morphological Filtering and CEEMDAN-WVD
The accuracy of the existing train wheel out-of-round monitoring methods is greatly affected by its speed and line conditions.In order to monitor the wheel service status more accurately,this paper proposes a wheel out-of-round diagnosis method based on morphological filtering and CEEMDAN-WVD:the vertical vibration acceleration of the axle-box of the vehicle is filtered by the mathematical morphology filter to reduce the noise,and then it is decomposed into a series of intrinsic modal functions(IMF)by the use of the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN).After decomposition into a series of intrinsic modal functions(IMF),the IMF components with relatively large energy entropy increments are selected for Wigner-ville distribution(WVD)calculation,so as to superimpose the multi-scale time-frequency diagrams of axle-box vibration acceleration.Finally,the distribution characteristics of the multi-scale time-frequency map are used to diagnose the wheel condition.The results of simulation analysis and engineering examples show that this method can be used to effectively identify the service state of wheels under complex working conditions.
wheel out of roundnessmorphological filteringcomplete noise assisted aggregation empirical mode decompositionWigner-Ville distributionmultiscale time-frequency map