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.
Highlights
The telemetry vibration signals are time series that include the system operating state, which are collected by sensors such as vibration acceleration or displacement, temperature and pressure installed in the aircraft
The adaptive decomposition method was used to decompose the non-stationary signal into several intrinsic mode functions (IMF) of instantaneous frequencies with physical significance, and a specific IMF was selected for resonance demodulation to extract the weak fault features, but the inherent problems of the adaptive decomposition method, such as mode aliasing, endpoint effect and pseudo-component, will restrict the effect of signal decomposition and affect the accuracy of feature extraction
In order to comprehensively and accurately detect the abnormality of the telemetry vibration signal, this paper proposes a transient feature extraction method based on adaptive tunable Q-factor wavelet transform (TQWT), which takes time–frequency distribution as the basis, and selects the characteristic frequency band to constrain the number of decomposition layers
Summary
The telemetry vibration signals are time series that include the system operating state, which are collected by sensors such as vibration acceleration or displacement, temperature and pressure installed in the aircraft. Wang et al [4] realized transient feature extraction of weak faults by modeling transient features and identifying transient signal model parameters with correlation filtering This modeling method requires a lot of prior information, so it is difficult to establish an accurate transient signal model in practice. The adaptive decomposition method was used to decompose the non-stationary signal into several intrinsic mode functions (IMF) of instantaneous frequencies with physical significance, and a specific IMF was selected for resonance demodulation to extract the weak fault features, but the inherent problems of the adaptive decomposition method, such as mode aliasing, endpoint effect and pseudo-component, will restrict the effect of signal decomposition and affect the accuracy of feature extraction
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