Multivariate local characteristic-scale decomposition and 1.5-dimensional empirical envelope spectrum based gear fault diagnosis

Most of the existing gear fault diagnosis methods only use the single-channel signal for processing. In order to extract more fault information and realize more comprehensive and accurate fault analysis, it is necessary to process the collected multi-channel signals. In this paper, a novel multivariate signal decomposition method, multivariate local characteristic-scale decomposition (MLCD) is proposed to decomposes multi-channel signal simultaneously. Comparing MLCD with multivariate empirical mode decomposition (MEMD), the results show that both methods are suitable for multivariate signal decomposition, but MLCD is superior to MEMD in computational efficiency, suppression of endpoint effect and decomposition accuracy. In order to highlight gear fault characteristic frequency, 1.5-dimensional empirical envelope spectrum (1.5D EES) is proposed. 1.5D EES combines the advantages of empirical envelope method and 1.5-dimensional spectrum, which can effectively reduce the noise of envelope signal and highlight the fault characteristics of signal. Based on the above two methods, a new gear fault diagnosis method, multivariate local characteristic-scale decomposition and 1.5-dimentional empirical envelope spectrum (MLCD-1.5D EES) is proposed and applied to multi-channel gear fault signal decomposition and fault feature extraction. Simulation and experimental results demonstrate the effectiveness and superiority of MLCD-1.5D EES.