Aiming at the problem of strong impact, short response period and wide resonance frequency bandwidth of transient vibration signals, a transient feature extraction method based on adaptive tunable Q-factor wavelet transform (TQWT) was proposed. Firstly, the characteristic frequency band of the vibration signal was selected according to the time–frequency distribution. Based on the characteristic frequency band, the sub-band average energy weighted wavelet Shannon entropy was used to optimize the number of decomposition layers, quality factor and redundancy of TQWT, so as to achieve the adaptive optimal matching of the impact characteristic components in the vibration signal. Then, according to the characteristics of the transient impact of the telemetry vibration signal, the TQWT decomposition coefficients were sparse reconstructed to obtain more sparse impact characteristics, and the weighted power spectrum kurtosis was used as the impact characteristic index to select the optimal sub-band, Finally, the inverse transform of TQWT was used to reconstruct the optimal sub-band to enhance its weak impact features. The simulation and measured signal processing results verify the effectiveness of the algorithm.

针对瞬态振动信号冲击大，响应周期短，谐振频率带宽大的问题，提出了一种基于自适应Q因子自适应小波变换（TQWT）的瞬态特征提取方法。首先，根据时频分布选择振动信号的特征频带。基于特征频带，利用子带平均能量加权小波香农熵来优化TQWT的分解层数，品质因数和冗余度，从而实现振动中冲击特征分量的自适应最优匹配。信号。然后，根据遥测振动信号的瞬态冲击的特征，将TQWT分解系数进行稀疏重构以获得更稀疏的冲击特性，并使用加权功率谱峰度作为冲击特性指标来选择最优子带，最后，使用TQWT的逆变换来重构最优子带。乐队增强了其微弱的冲击功能。仿真和实测信号处理结果验证了该算法的有效性。