Refined matching linear chirplet transform for exhibiting time-frequency features of nonstationary vibration and acoustic signals.

• Transformation kernel is deduced to process multicomponent signal measured from rotating machinery. • Prior instantaneous frequency knowledge of the machinery component is not required. • Angle refinement step is developed to improve the capability of handling noise. • Chirp rates (angles) are adaptively selected by spectral kurtosis index. Time-frequency (TF) features of nonstationary vibrations are indicative of the health condition of rotating machinery and, are also pivotal in analyzing acoustic signals obtained from processes such as bat echo-location. However, the TF features in these nonstationary vibration and acoustic signals are often submerged by strong background noise. This article proposes using the refined matching linear chirplet transform (RMLCT) to enhance the TF features, where the chirp rates are adaptively determined by spectral kurtosis and only the interesting time-frequency representations (TFRs) are retained. With selected chirp rates, a novel transformation kernel is developed, enabling the proposed method to simultaneously process nonstationary multicomponent signals. Moreover, the angle refinement strategy is proposed to improve the noise-handling capability of the proposed method. The signal reconstruction of the RMLCT is also analyzed, demonstrating that signal components of interest can be accurately reconstructed. Numerical and experimental analyses validate the effectiveness of the proposed RMLCT. [ABSTRACT FROM AUTHOR]