Local Maximum Synchrosqueezing Chirplet Transform: An Effective Tool for Strongly Nonstationary Signals of Gas Turbine

Time–frequency (TF) analysis (TFA) provides an effective tool to characterize nonstationary signals with time-varying features. However, the TFA of gas turbine's vibration signals is a challenging topic due to high complexity and strong nonstationarity. There is an obstacle to generate more accurate and sharper TF results for such multicomponent signals. This article proposes a novel TFA technique, named local maximum synchrosqueezing chirplet transform (LMSSCT), to deal with this problem. This method can not only well match window function and modulated frequency but produce an unbiased instantaneous frequency (IF) estimator to correct the deviation caused by strong frequency modulation (FM) in TF results. We give the theoretical analysis that this method is an improvement of classical local maximum synchrosqueezing transform (LMSST), and we also prove that it allows for perfect signal reconstruction. The numerical validation shows that the proposed method can be employed to effectively address the multicomponent signals with complex FM laws, even those with heavy noise. The experimental analysis on the test-bench signal and the vibration signal of a dual-rotor gas turbine validates that this method can capture more detailed features that are helpful to identify the origins of abnormal vibration of gas turbine.