Resonance-based sparse improved fast independent component analysis and its application to the feature extraction of planetary gearboxes

Abstract

A resonance-based sparse improved fast independent component analysis (ICA) (RSIFICA) is proposed to extract the fault characteristics of planetary gearbox. First, the signal is decomposed using resonance sparse signal decomposition (RSSD). Second, high-resonance components were retained while others were eliminated. Finally, the signal after dimension reduction was analyzed using ICA, and the fault characteristic frequency was extracted through envelope spectrum analysis. In this process, the preset Q parameter of RSSD is optimized on the basis of fuzzy entropy and ant-lion optimization algorithm. The accuracy of RSSD was improved by performing time-frequency entropy component selection. FastICA was improved, and the slow convergence problem of ICA was solved. Results showed that RSIFICA could extract the fault characteristic frequency accurately, and the calculation efficiency of FastICA increased by 21.49 %. In terms of extracting the fault features, its performance could be better than EMD-FastICA.