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In the fault period of high-speed train lateral damper, the vibration signal is non-linear and nonstationary, and features extracting is relatively difficult. In order to save this problem, a method of features extracting based on variational mode decomposition and multiscale entropy was proposed. The original signal was decomposed into several intrinsic mode function components after being processed by the variational mode decomposition mothed. Then, the best component was selected by the mutual information index. The feature matrix was constructed through the multiscale entropy of the best component, and removed redundant features using feature evaluation algorithm. The fault type of lateral damper was judged by transforming in the best subset of feature matrix in support vector machine. Experimental results show that the proposed method can extract the feature and judge the fault type of lateral damper effectively, which proves the feasibility of this mechanical fault diagnosis method.
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