Thrust bearing groove race defect measurement by wavelet decomposition of pre-processed vibration signal

Condition Monitoring and Fault Diagnosis for an Antifalling Safety Device

“…The focus is on the comparative study of various broadband input excitations. Measurement of groove race defect width in thrust bearing is presented by processing vibration signal using wavelet based technique by Singh and Kumar [19]. Wavelet features are computed using discrete wavelet transform (DWT) from the vibration signals of centrifugal pumps by Muralidharan and Sugumaran [20].…”

Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine

“…Various signal processing and pattern recognition approaches are proposed as available diagnostics tools [1]. It is worthy to note that timefrequency analysis including wavelet analysis [2][3][4][5] and HilbertHuang transform [6][7][8] can reveal defect frequency characteristics of the vibration signal. However, there are a number of factors affecting the complexity of the bearing signal [9].…”

Rolling element bearing fault detection using PPCA and spectral kurtosis

“…The reason is that series of periodic impact load will be generated when every rolling element passes through the damage position. Generally, the bearing fault can be detected according to this modulation frequency [1][2][3][4][5][6][7]. However, these modulation signals are commonly carried by a high frequency signal and are easily contaminated by environmental noise.…”

“…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].…”