High-dimensional optimized extraction chirplet transform: Algorithm and applications

Under time-varying operating conditions, vibration signals from rotating machinery typically contain dense and non-proportional frequency components, which make it difficult for current time-frequency analysis (TFA) techniques to achieve high-precision and highly concentrated time-frequency representations (TFRs). Accordingly, the high-dimensional optimized extraction chirplet transform (HOECT) is proposed. In the HOECT, a window parameter adaptive optimization criterion is first designed to dynamically match local characteristics of the signal; second, the component matching CT (CMCT) is improved based on the proposed criterion, and its results are mapped into the time-frequency-chirprate domain to separate frequency components; finally, based on estimated local frequency and chirprate, the smoothing extraction operator (SEO) is constructed in the frequency-chirprate domain, which significantly enhances the energy concentration. In addition, a novel connected domain analysis (CDA) algorithm is proposed to effectively suppress strong noise interference and highlight key features of signal components. Simulation results illustrate that the HOECT can accurately characterize dense and non-proportional frequency components with high energy concentration. The superiority of the HOECT in operational state characterization and fault feature identification is further verified through two vibration signals of planetary gearboxes. Additionally, the whale sound signal results indicate that the proposed method exhibits strong robustness and applicability in analyzing complex non-stationary signals.