Transient feature extraction method based on adaptive TQWT sparse optimization

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