Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet

Rolling element bearing fault detection using PPCA and spectral kurtosis

“…The shaft rotating frequency is exactly equal to 30 Hz and fault frequency 148.4 Hz and its harmonic frequency 296.9 Hz is clearly demodulated, which is matched with the theoretical calculation value 148.4 Hz (show in Eq. (18)). Therefore, the bearing fault for inner race is detected.…”

“…However, a great deal of fault samples should be given to perform these methods. The second directly employs signal processing method to extract feature frequencies, which have achieved a great progress [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]18,19]. The most effective method is the envelope analysis (EA), and the effectiveness of this method depends on how well the vibration signal is preprocessed and what denoising method is adopted.…”

“…The most effective method is the envelope analysis (EA), and the effectiveness of this method depends on how well the vibration signal is preprocessed and what denoising method is adopted. Recently, wavelet transform has been proved to be a good choice for impact signal denoising [1][2][3][4][5][6][7][18][19][20][21][22][23]. Empirical mode decomposition (EMD) and the further improvement methods were explored for reducing the white noise [23][24][25][26][27][28].…”

Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed

“…An improved EEMD with multi-wavelet packet method is proposed by Jiang [3], and the experiment results show that the improved method is a good solution to address the issue of rolling element bearing multi-fault diagnosis. Chen [4] developed a novel compound faults detection method based on the improved adaptive redundant lifting multi-wavelet and Hilbert transform demodulation analysis, analysis results show the effectiveness and reliability on the compound faults detection. However, these methods cannot be directly applied to bearing fault diagnosis under variable rotational speed conditions.…”

Planetary gearbox condition monitoring of ship-based satellite communication antennas using ensemble multiwavelet analysis method

“…Because it possesses multiple wavelet basis functions, multiwavelet does well in extracting features with multiple kinds of shape for condition feature extraction. However, an inappropriate mother wavelet employed in the engineering application often lower the accuracy of the fault detection and condition identification [22]. So it is very important to select an appropriate wavelet basis for the signal processing.…”