According to the performance degradation problem of feature extraction from higher-order statistics in the context of alphastable\nnoise, a new feature extraction method is proposed. Firstly, the nonstationary vibration signal of rolling bearings is\ndecomposed into several product functions by LMD to realize signal stability. ,en, the distribution properties of product\nfunctions in the time domain are discussed by the comparison of heavy tails and characteristic exponent estimation. Fractional\nlower-order p-function optimization is obtained by the calculation of the distance ratio based on K-means algorithms. Finally, a\nfault feature dataset is established by the optimal FLOS and lower-dimensional mapping matrix of covariation to accurately and\nintuitively describe various bearing faults. Since the alpha-stable noise is effectively suppressed and state described precisely, the\npresented method has shown better performance than the traditional methods in bearing experiments via fractional lower-order\nfeature extraction.
Loading....