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Time-frequency (TF) analysis (TFA) to nonstationary signals can reveal the nonlinearly changing instantaneous frequencies (IFs) of signals; it is, therefore, widely used for rotating machinery fault diagnosis under time-varying speed conditions. However, the traditional TFA methods may only reveal the outline of IFs due to the extra TF diffusion caused by limited TF resolution, hindering the fault diagnosis of rotating machinery. To enhance the readability of the time-frequency representation (TFR) of the signals with IF trajectories linearly time-varying, linear chirplet transform has been proved effective. To effectively tackle the signal with nonlinearly changing IFs, a string of chirp-rates is preferred, where the final TFR is obtained by superposition of each corresponding sub-TFR at each TF point. However, the extra cross-term interferences resulted from a string of chirp-rates cannot be neglected on TFRs. Aiming at alleviating the cross-terms from the TFR, matching and reassignment based TF enhancement strategy is proposed, where only the appropriate chirp-rate and its corresponding TFR slice is retained at each time instant. The appropriate chirp-rate is adaptively selected by the index—spectral kurtosis. To further increase the readability of the resulting TFR, a reassignment technique synchrosqueezing transform is integrated with the proposed matching strategy. By iteratively employing reassignments, TFR with the enhanced energy and sharp IF ridges can be generated. The effectiveness of the proposed method is validated by both simulated and experimental analyses. It is shown that the proposed method is effective in processing time-varying signals and can provide more accurate IF estimation, which paves the way for rotating machinery fault diagnosis under nonstationary speed conditions. [ABSTRACT FROM AUTHOR]

• 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]