# Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD
## 4 Fault Feature Extraction and Enhancement Method Based on OCYCBD and SVDD  
In practical engineering, the bearing injury signature is usually inconspicuous and covered by strong external noises in the early stage. For a given signal of rolling bearing with microlesion, if the impulsive fault source can be recovered from the collected observed signal, then the SNR can be improved drastically and the weak fault feature can be extracted preferably. Owing to the excellent ability of eliminating the spreading effect of unknown transmission path, the above proposed OCYCBD technology is regarded as a pre-processing method to achieve this purpose. And this will facilitate the follow-up feature identification progress. FWEO is capable of tracking and intensifying the impulsive feature. Moreover, compared with the traditional energy operator algorithm, it is more robust against severe interference noises. Thus, FWEO is further conducted to calculate the instantaneous energy signal of deconvolution signal, which can presents the regular impulsive feature better. Nevertheless, some interference components still remain in the instantaneous energy signal because the SNR of collected fault signal is very low, and the defect identification is easily affected by these redundant interference components. Thus, for the purpose of enhancing the fault signature and getting rid of the remained interferences, SVDD is applied as a post-processing approach to purify the instantaneous energy signal of deconvolution signal. On the basis of these statements, a novel feature extraction and enhancement method is put forward to improve the accuracy of defect identification for wind turbine bearing. Figure 6 illustrates the schematic of this method, and the diagnosis procedures are as follows:  
1. Data acquisition. The original signal is collected using the corresponding data acquire equipments.
2. OCYCBD processing. The fast spectral coherence and the equal step size search strategy are combined organically to search for the optimal cycle frequency and the filter length, and the corresponding steps have been elaborate explained in Section 2.4. The obtained optimal parameters are substituted into CYCBD. Then the informative fault source with higher SNR can be recovered from the original collected signal by deconvolution operation.
3. Instantaneous energy signal calculation. The FWEO operation is carried out on the obtained deconvolution signal to calculate the instantaneous energy signal. The impulsive signature of the instantaneous energy signal is more outstanding than the original signal, but there still exist the interference components. Then the following procedure is carried out to get rid of the redundant components in the instantaneous energy signal and enhance the defect signature.
4. SVDD processing. The SVDD approach is further utilized for instantaneous energy signal denoising. And the effective singular value order to reconstruct the matrix can be determined using the fitting error minimum principle. Because the quadratic polynomial fitting algorithm is utilized to establish the fitting functions, thus the initial order is set to \(r\) = 4. And the denoised instantaneous energy signal can be gained through the diagonal mean operation of reconstructed matrix.
5. Enhanced energy spectrum analysis. The Fourier transform based spectrum analysis is performed on the purified instantaneous energy signal of deconvolution signal, and the corresponding enhanced energy spectrum of deconvolution signal can be acquired. Then the defect location of wind turbine bearing is able to be judged by analyzing the spectral peak in the enhanced energy spectrum.