Adaptive synchrosqueezing chirplet group decomposition and its application in gear fault diagnosis

Traditional signal decomposition methods, such as ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD) and singular spectrum decomposition (SSD), can decompose a multicomponent signal into several simple components. However, when the signals are very complicated and crossover instantaneous frequencies exist, traditional methods often lose their effects. Because of the complexity of the mechanical structure and the interferences of noise, the vibration signals of the rotary machine are very complicated, and may contain crossover instantaneous frequencies. Thus, traditional methods may fail to get accurate results. In order to solve the problem above, a new signal decomposition method named as adaptive synchrosqueezing chirplet group decomposition (ASSCGD), is proposed and applied to gear fault diagnosis. ASSCGD firstly use adaptive chirplet transform to get the time–frequency-chirp-rate representation of the signal. Then, synchrosqueezing transform under time-varying parameter is used to further concentrate the energy of the representation. Next, ASSCGD extract 3-d ridge lines of all components through the ridge search algorithm. The decomposition results are finally obtained through group decomposition method. The analysis of simulation signals and experimental bevel gear signals verifies the effectiveness of ASSCGD in gear fault diagnosis.