Abstract
The fault feature of the rolling bearing is difficult to extract when weak fault occurs and interference exists. The tunable Q-factor wavelet transform (TQWT) can effectively extract the weak fault characteristic of the rolling bearing, but the manual selection of the Q-factor affects the decomposition result and only using TQWT presents interference. Aiming at the above problems, an adaptive tunable Q-factor wavelet transform (ATQWT) and spectral kurtosis (SK) method is proposed in this paper. Firstly, the method applies particle swarm optimization (PSO) to seek the optimized Q-factor for avoiding manual selection, which uses the kurtosis value of the transient impact component as the particle fitness function. The rolling bearing fault signal is decomposed into continuous oscillation component and transient impact component containing fault feature by the optimized Q-factor. Then, due to the presence of interference in the decomposition result of ATQWT, the SK analysis of the transient impact component is used to determine the frequency band of periodic impact component characterizing fault feature by fast kurtogram. Finally, the band-pass filter established through the above frequency band is employed to filter the interference in the transient impact component. Simulation and experimental results indicate that the ATQWT can highlight the periodic impact component characterizing rolling bearing fault feature, and the SK can filter interference in the transient impact component, which improves feature extraction effect and has great significance to enhance fault diagnosis accuracy of the rolling bearing. Compared with EEMD-TQWT and TQWT-SK, the fault feature extracted by the proposed method is prominent and effective.
Highlights
Rolling bearing, a harsh working environment and high incidence of failure, is a critical part of rotating machinery
In view of the advantages of TQWT and spectral kurtosis (SK) in extracting rolling bearing fault feature, this paper proposes a fault feature extraction method based on adaptive tunable Q-factor wavelet transform (ATQWT) and SK. e high and low factors are optimized by particle swarm optimization (PSO), and the optimized Q-factor is used to perform tunable Q-factor wavelet transform to highlight the periodic impact component in the transient impact component; the SK analysis of the transient impact component determines the frequency range of the periodic impact component. e filter established by the above frequency band can filter out the interference component in the transient impact component, highlight the fault feature, and improve the accuracy of fault diagnosis of rolling bearing
In order to verify the effectiveness of the ATQWT and SK in rolling bearing fault feature extraction, a simulation signal x is simulated to the rolling bearing fault vibration signal, where x1 with a frequency of 100 Hz simulates the periodic impact component characterizing rolling bearing fault feature, and the sinusoidal signals with a frequency of 30 Hz and 2358 Hz express the rotational frequency and harmonic, and the Gaussian white noise w is used as the background noise with the intensity of −9db: x1(t) π, 2 π
Summary
A harsh working environment and high incidence of failure, is a critical part of rotating machinery. E resonance is excited on account of impact pulse force covered high-frequency natural vibration of the rolling bearing, and its attenuation time is much smaller than the interval between the impulse forces, generating a series of periodic impact components [8]. On account of the problems existing in the above feature extraction methods, Selesnick proposed the tunable Q-factor wavelet transform (TQWT), which can decompose the vibration signal into the continuous oscillation component and the transient impact component according to the difference of pre-set Q-factor [12, 13]. We can find that the periodic impact component characterizing the rolling bearing fault feature is not obviously directly obtained by the TQWT, has a lot of interferences, and is influenced by the selected Q-factor. In view of the advantages of TQWT and SK in extracting rolling bearing fault feature, this paper proposes a fault feature extraction method based on adaptive tunable Q-factor wavelet transform (ATQWT) and SK. e high and low factors are optimized by particle swarm optimization (PSO), and the optimized Q-factor is used to perform tunable Q-factor wavelet transform to highlight the periodic impact component in the transient impact component; the SK analysis of the transient impact component determines the frequency range of the periodic impact component. e filter established by the above frequency band can filter out the interference component in the transient impact component, highlight the fault feature, and improve the accuracy of fault diagnosis of rolling bearing
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