基于自适应线调频基原子分解的时域同步平均方法及其在变转速齿轮故障诊断中的应用

提出一种基于自适应线调频基原子分解(adaptive chirplet atomic decomposition,ACAD)的时域同步平均方法,并将其应用于低信噪比下变转速齿轮故障诊断。首先对齿轮振动信号进行ACAD分解估计齿轮所在轴的转速曲线;然后根据转速曲线对信号进行等角度重采样,以满足时域同步平均方法对信号周期平稳的要求;再利用时域同步平均方法对重采样信号进行处理,处理后的信号具有很高的信噪比;最后,对其进行FFT变换,其阶次谱上非常清晰地显示齿轮的调制阶次,从而揭示齿轮的故障信息。仿真算例与应用实例证明了该方法的有效性。

A Time Synchronous Average Method Based on Adaptive Chirplet Atomic Decomposition and Its Application in Gear Fault Diagnosis with Large Rotating Speed Variation

A time synchronous average method based on adaptive chirplet atomic decomposition(ACAD) was proposed to deal with the non-stationary vibration signals of fault gears.The stationary requirements were satisfied for TSA method by resampling the signals in equal angle according to the rotating speed curve obtained by the ACAD mthod.The SNR was improved by processing the resampled signals with TSA method.The modulation orders of gears can be shown in the order spectrum clearly with the FFT transform,and it reveals the types of a gear’s faults. Simulation and application examples proved the effectiveness of the method.