A Fault Diagnosis Method for One-Dimensional Vibration Signal Based on Multiresolution tlsDMD and Approximate Entropy

Abstract

Dynamic mode decomposition (DMD) has certain advantages compared with the traditional fault signal diagnosis method. By exploiting the strength of DMD algorithm in signal processing, this paper proposes a joint fault diagnosis scheme to extract the spatial and temporal patterns and evaluate them for the complexity to diagnose the fault for one-dimensional mechanical signal. The multiscale method is adopted to decompose the reconstructed matrix of standard DMD modes into multiple scales with a given level parameter. Total least squares DMD algorithm is performed on each level to solve the noise sensitivity problem. Approximate entropy (ApEn) is performed on the grouped multiscale spatiotemporal modes that represent the dynamic characteristic information of the original signal. ApEn values are used as a fault recognizer to identify fault types. By applying the algorithm on three experimental mechanical vibration data, we verify the effectiveness of the proposed method. The result demonstrates that the proposed scheme can effectively recognize different fault forms as a fault diagnosis method.