# Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD
## 2 Optimized Cyclostationary Blind Deconvolution
### Research on the Influences of Key Parameters
After that, the influence of cyclic frequency parameter \(a\) is further discussed when filter length is set to \(L=150\). The identical simulated fault signal is processed by CYCBD under the conditions of selected  
Figure 2: The waveforms and the envelope spectra of deconvolution signals obtained by CYCBD with different filter lengths and fixed cyclic frequency: (**a**) The waveform corresponding to \(L=50\); (**b**) The envelope spectrum of (**a**); (**c**) The waveform corresponding to \(L=100\); (**d**) The envelope spectrum of (**c**); (**e**) The waveform corresponding to \(L=150\); (**f**) The envelope spectrum of (**e**); (**g**) The waveform corresponding to \(L=200\); (**h**) The envelope spectrum of (**g**). cyclic frequencies deviating from the authentic feature frequency 120 Hz, and the obtained results when cyclic frequency \(a\) is set to 120, 120.1, 120.5 and 121 are respectively displayed in Figure 3. Based on these results, we can find the first deconvolution signal obtained by CYCBD with \(a=120\) are closely similar to the second deconvolution signal when \(a=120.1\). The FER index of the second deconvolution signal stills remain large value 21.89% and the feature frequency components are also effectively discovered from the simulated signal when the cyclic frequency deviation reaches to 0.1 Hz (0.0833% of fault feature frequency), which means CYCBD has definite tolerance to inaccurate cyclic frequency. However, Figure 3f,h illustrate the FER index decreases rapidly when the deviation comes up to 0.5 Hz (0.4167% of fault feature frequency), and CYCBD fails to discover feature information when the predetermined cyclic frequency \(a=121\) deviates from the real value up to 0.833%. Then the comparison results show the cyclic frequency must be set as precise as possible to get the favorable result. Figure 3: The waveforms and the envelope spectra of deconvolution signals obtained by CYCBD with different cyclic frequencies and fixed filter length: (**a**) The waveform corresponding to \(a=120\); (**b**) The envelope spectrum of (**a**); (**c**) The waveform corresponding to \(a=120.1\); (**d**) The envelope spectrum of (**c**); (**e**) The waveform corresponding to \(a=120.5\); (**f**) The envelope spectrum of (**e**); (**g**) The waveform corresponding to \(a=121\); (**h**) The envelope spectrum of (**g**). Based on the above discussions, it is indicated that the performance of CYCBD on bearing fault signal processing is seriously depended on the setting filter length parameter and the cyclic frequency parameter, and the influence of cyclic frequency is more serious compared with filter length. To effectively acquire the optimal feature extraction result, a novel OCYCBD method is put forward in the subsequent section, in which the optimal influencing parameters is confirmed by fusing the fast spectral coherence with the equal step size search strategy, and the whole optimization process is guided by the proposed frequency weighted energy entropy indicator.