Transient feature extraction method based on adaptive TQWT sparse optimization

Abstract

Abstract 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.

Keywords

• Transient vibration signal
• Time–frequency distribution
• Tunable Q-factor Wavelet Transform
• Kurtosis
• Power spectrum kurtosis
• Shannon entropy