Improved Group Sparse Modal Decomposition Methods With Applications to Fault Diagnosis of Rotating Machinery

Group-sparse mode decomposition (GSMD) is an efficient signal decomposition algorithm for separating harmonic signals, but fails to split modes for periodic pulse signals. In order to address the limitations of the GSMD algorithm in dealing with periodic pulse signals, this study proposes two improved GSMD methods, namely adaptive adjusted bandwidth sparse mode decomposition (AABSMD) and adaptive Gaussian window sparse mode decomposition (AGWSMD). The AABSMD method utilizes an iterative least-squares curve fitting approach to plot the energy spectrum and adjusts the filter using a -3 dB bandwidth, which avoids unreasonable bandwidth estimation. The AGWSMD method employs a segmented quantile regression method to fit the energy spectrum and utilizes a Gaussian window as a filter to extract the signal, which selects energy entropy as the parameter to optimize the Gaussian function. Both methods overcome the defects of splitting periodic pulse signals in GSMD and provide more accurate determination of the number of modes, resulting in a well-performed decomposition. In addition, the AGWSMD method exhibits outstanding reconstructive performance and high efficiency. Numerical and experimental results indicate the effectiveness and superiority of the proposed methods, which can be successfully applied to fault diagnosis of rotating machineries.