Improved multiscale weighted-dispersion entropy and its application in fault diagnosis of train bearing

Abstract Multiscale dispersion entropy (MDE) is a common method for measuring the complexity of nonlinear time series. However, the uncertainty results by the MDE tool may be unreliable as the coarse-graining procedure will reduce the number of data points at a large scale. In addition, the essential differences between the matching patterns cannot be extracted by MDE. To effectively alleviate the above limitations of MDE, an improved multiscale weighted-dispersion entropy (IMWDE) method is proposed in this article. Weight coefficients and weight probabilities were assigned to each vector to consider the amplitude information, and an improved coarse grained process is proposed for entropy value refinement. The performance of the IMWDE method is evaluated with synthetic data. Based on a powerful algorithm for key feature extraction, a novel intelligent diagnosis technique is proposed by combining classifiers. Finally, real vibration signals collected from axle-box bearings are used to demonstrate the effectiveness of the diagnosis scheme. Compared with MDE and IMWDE, the results indicate that the proposed method achieves smaller errors, and the highest diagnosis accuracy.