Frequency-chirprate synchrosqueezing-based scaling chirplet transform for wind turbine nonstationary fault feature time–frequency representation

Abstract

As key rotational components of wind turbines, planetary gearboxes, and bearings need special health monitoring and fault diagnosis for reducing downtime and maintenance costs. However, it is still a challenging issue for time–frequency analysis (TFA) techniques to analyze nonstationary and close-spaced fault frequencies of wind turbines. Hence, a high- concentration TFA technique, termed frequency-chirprate synchrosqueezing-based scaling chirplet transform (FCSSCT) is developed. In the FCSSCT, the novel dimension of chirprate is introduced to map the signal into the three-dimensional space of time–frequency-chirprate in contrast to the time–frequency domain in the synchrosqueezing transform (SST), herein, the three-dimensional space of time, frequency and chirprate is calculated based on the scaling-basis chirplet transform (SBCT); then, a frequency-chirprate synchrosqueezing operator (FCSO) is defined in the frequency-chirprate domain to reassign the amplitude coefficients of the SBCT results and a three-dimensional representation of time–frequency-chirprate is obtained; finally, the time–frequency representation (TFR) with concentrated energy is obtained by transforming the three-dimensional representation of the time–frequency-chirprate space into the time–frequency domain. The effectiveness of the developed FCSSCT is verified by the simulated multi-component signals with close-spaced frequencies or crossover frequencies. Experimental analysis results on wind turbine planetary gearbox and bearing show that the developed technique is effective in characterizing nonstationary fault frequencies. Rényi entropies demonstrate that the FCSSCT has much better energy concentration compared with several advanced TFA methods.