# Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD
## 5 Experiment Verification
### Experimental Signal Analysis and Result Comparison
Then the feasibility of proposed method in getting rid of the severe background noises and intensifying the weak fault signature of rolling bearing is verified through this experimental signal. Figure 8: The collected experimental signal: (**a**) The waveform of experimental signal; (**b**) The frequency spectrum of experimental signal; (**c**) The envelope spectrum of experimental signal. To clearly verify the advantage of fusing OCYCBD with SVDD on noise interference suppression and fault feature enhancement, the energy spectrum of deconvolution signal without denoising operation is displayed in Figure 10. It is indicated that the fault feature frequency components are disturbed by background noises and interference spectral peaks, and this isn't conducive to achieve precise bearing flaw detection. Owing to the favorable specialty of SVDD, the redundant interference components in the instantaneous energy signal can be removed absolutely, and the fault feature spectral lines in the enhanced energy spectrum can be presented more obvious. In order to further demonstrate the necessity of equal step size search strategy for precise cyclic frequency selection, the parameter \(a=114.7\) which is confirmed only by the fast spectral coherence, is  
Figure 10: The energy spectrum of deconvolution signal without denoising operation. Figure 9: Analysis results of experimental signal by proposed method: (**a**) The fast spectral coherence; (**b**) The MFWFE values corresponding to different cyclic frequencies; (**c**) The FWEE indicators corresponding to different filter lengths; (**d**) The waveform of deconvolution signal obtained by OCYCBD; (**e**) The fitting error values corresponding to different singular value orders; (**f**) The enhanced energy spectrum of deconvolution signal. substituted into CYCBD. And the filter length is still set to \(L=117\). The obtained results by CYCBD with these two parameters are displayed in Figure 11. It can be found the analysis results are non-ideal because the periodic impact components in the waveform aren't abundant and the feature spectral lines in the energy spectrum are vague, which hinder the determination of bearing defect location. It is indicated that the diagnosis results may be entirely different due to the imprecise selection of influencing parameters. Fortunately, we can favorably achieve the aureate fault identification with the help of proposed parameter optimization strategy. To demonstrate the superiority of proposed method, the well known SK method, MED method and MCKD method are respectively applied for comparison. Figure 12a illu states the calculated kurtosis, and the optimal resonant band in level 3 is marked by black oval. Based on the obtained information in the kurtosis, the center frequency and the bandwidth of designed filter are 400 Hz and 800 Hz. Nerveless, only the harmonics of rotating frequency can be seen in Figure 12b. The fault feature frequency isn't discovered due to the selected filter frequency band is unreasonable. The analysis results using MED and MCKD are shown respectively in Figures 13 and 14. The key parameters of there two methods are confirmed based on the obtained search results in Figure 9b,c. As the optimal cyclic frequency is \(a^{o}=115.7\) and the optimal filter length is \(L^{o}=117\), thus the deconvolution period of MCKD is calculated as \(fs/a^{o}=110.63\), and the filter lengths of MED and MCKD are both set to 117. The interval of periodic impact in Figure 13a isn't related to bearing defect, and the fault feature frequency of roller isn't able to be found from the corresponding envelope spectrum in Figure 13b. Thus, the analysis results of MED method losing their practical meanings. It is noticeable that the fault signatures in Figure 14a aren't as outstanding as those in Figure 9d, the basic fault frequency spectral line and a lot of unrelated components are simultaneously appear in Figure 14b. But the harmonics of feature frequency aren't detected and the obtained envelope spectrum is also non-ideal.