Energy bubble entropy guided symplectic geometry mode decomposition for rotating machinery incipient fault feature extraction

Abstract Extracting incip i ent fault features is a critical aspect of monitoring the rotating machinery operation condition. However, existing methods based on symplectic geometry mode decomposition (SGMD) suffer from limited parameter adaptability and noise robustness. Therefore, this paper proposes an energy bubble entropy (EbEn) guided SGMD method to extract incipient fault feature. Firstly, the SGMD method is employed to initially separate fault characteristic components from noisy signal. Furthermore, the EbEn is constructed to evaluate the attributes of incipient feature within the signal, which requires almost no parameter setting with good robustness and computational efficiency. Thirdly, the empirical bayes shrinkage method effectively mitigates irrelevant noise and enhances the significance of incipient fault feature. Simulated and experimental signals are employed to substantiate the efficacy of the EbEn guided SGMD method. The comparison analysis with relevant methods exhibits that this method has greater robustness and adaptivity.