Application of the horizontal slice of cyclic bispectrum in rolling element bearings diagnosis

Zhou, Yu; Chen, J.; Dong, Guangming; Xiao, W.B.; Wang, Z.Y.

doi:10.1016/j.ymssp.2011.07.006

Cited by 60 publications

(43 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The key idea of the bicoherence analysis is that, due to the faults extending of the mechanical structure, the vibration signature will tend to be more nonlinear which will result in the generation of new frequencies [13]. Those new frequencies are phase coupled to the original characteristic frequencies of the structure, and the wavelet bicoherence is a sensitive detector for such phase coupling identification [14][15][16]. Compared with the Fourier bicoherence, WB has its advantage in preservation of temporal information, thus it is more suitable for the instantaneous vibration signal analysis.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rolling element bearing using nonlinear wavelet bicoherence features

Wang

Lin

2014

2014 International Conference on Prognostics and Health Management

View full text Add to dashboard Cite

Unexpected bearing failures may cause unscheduled downtime and economic losses. It is, therefore, very important to find the faults symptoms of the rolling element bearing components. Vibration signal of fault bearing is nonlinear and non-stationary in nature, which makes the stationary assumed methods not appropriate. In this paper, a biphase randomization wavelet bicoherence method is introduced, which combines benefits of the wavelet transform and the bicoherence analysis. By simultaneously using the amplitude of the continuous wavelet transform and biphase information, this method can eliminate the spurious bicoherence coming from long coherence time waves and non phase coupling waves efficiently. Based on this method, two quadratic nonlinearity features are proposed for fault diagnosis of rolling element bearing. At the same time, the proposed features are applied to the real-world vibration data collected from locomotive roller bearings with faults on inner race, outer race and rollers, respectively. Experiment results demonstrate that the performance of the proposed features is much better than that of some original features.

show abstract

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rolling element bearing using nonlinear wavelet bicoherence features

Wang

Lin

2014

2014 International Conference on Prognostics and Health Management

View full text Add to dashboard Cite

show abstract

“…These faults may enhance vibration and noise level [12]. Moreover, there are internal operating stresses caused by vibration, eccentricity, and bearing current.…”

Section: Bearing Defects (Bds) Signaturesmentioning

confidence: 99%

“…Relatively, some literatures traiting the bi-spectrum diagnosis of BDs have been published, in [12] the twice slice of cyclic bispectrum (CBS) is used for the detection of BDs. This method Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/isatrans inherits the advantage of the CBS, and is not sensitive to noise.…”

Section: Introductionmentioning

confidence: 99%

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis

2014

View full text Add to dashboard Cite

“…Because of the structure and the mode of operation of REBs, x(t) has distinct features as follows [6], [11]: -Wide frequency range: BDs usually start as small pits or spalls, and give sharp impulses in the early stages covering a very wide frequency range. -Small energy: The energy created by the BD is very small.…”

Section: A the Characteristics Of Rolling Bearing Signalsmentioning

confidence: 99%

“…Relatively, some literatures trating the bi-spectrum diagnosis of BDs have been published, in [11] the twice slice of cyclic bi-spectrum (CBS) is used for the detection of BDs. This method inherits the advantage of the CBS, and is not sensitive to noise.…”

Section: Introductionmentioning

confidence: 99%

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis

Saïdi

Ali

Fnaiech

et al. 2014

2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR)

View full text Add to dashboard Cite

Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always nonstationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it's flat for nonGaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bispectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of intrinsic mode functions (IMFs) is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures.

show abstract

Application of the horizontal slice of cyclic bispectrum in rolling element bearings diagnosis

Cited by 60 publications

References 14 publications

Fault diagnosis of rolling element bearing using nonlinear wavelet bicoherence features

Fault diagnosis of rolling element bearing using nonlinear wavelet bicoherence features

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis

Contact Info

Product

Resources

About