Multivariate local fluctuation mode decomposition and its application to gear fault diagnosis

In this paper, we propose a novel method, called multivariate local fluctuation mode decomposition (MLFMD), to improve the accuracy and efficiency of fault diagnosis using multiple channels signals. Compared with multivariate empirical mode decomposition (MEMD), MLFMD uses second-order differentiable local extreme point localization (SDLEPL) to mine the local hidden information and an adaptive non-uniform projection (ANP) technique to improve the decomposition accuracy. In addition, the MLFMD method employs multivariate periodic mean to extract the mean curves, which improves the decomposition efficiency. Compared with traditional MEMD, our proposed MLFMD algorithm has higher decomposition accuracy and efficiency. Furthermore, a new fault diagnosis method based on MLFMD is proposed, which can efficiently fuse data from each channel. The efficacy of the proposed method is validated with both simulated and real-world signals, and the results demonstrate the superiority of the MLFMD.