# Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD
###### Abstract  
The fault feature of wind turbine bearing is usually very weak in the early injury stage, in order to accurately identify the defect location, an original approach based on optimized cyclostationary blind deconvolution (OCYCBD) and singular value decomposition denoising (SVDD) is put forward to extract and enhance the fault feature effectively. In this diagnosis method, the fast spectral coherence is fused with the equal step size search strategy for the cyclic frequency parameter and the filter length parameter optimization, and a new frequency weighted energy entropy (FWEE) indicator which combining the advantages of the frequency weighted energy operator (FWEO) and the Shannon entropy, is developed for deconvolution signal evaluation during parameter optimization process. In addition, a novel singular value order determination approach based on fitting error minimum principle is utilized by SVDD to enhance the fault feature. During the process of defect identification, OCYCBD with the optimal parameters is firstly used to recover the informative source from the collected vibration signal. FWEO is further utilized to highlight the potential impulsive characteristics, and the instantaneous energy signal of deconvolution result can be acquired. The whole interferences contained in the instantaneous energy signal can't be removed due to the weak fault signature and the severe background noise. Then, SVDD is applied to purify the instantaneous energy signal of deconvolution signal, by which the residual interference component is eliminated and the fault feature is strengthened immensely. Finally, frequency domain analysis is performed on the denoised instantaneous energy signal, and the defect location identification of wind turbine bearing can be achieved through analyzing the obvious spectral lines in the obtained enhanced energy spectrum. The collected signals from the experimental platform and the engineering field are both utilized to verify the feasibility of proposed method, and its superiority is further demonstrated through comparing with several well known diagnosis methods. The results indicate this novel method has distinct advantage on bearing weak feature extraction and enhancement. optimized cyclostationary blind deconvolution; frequency weighted energy entropy; singular value decomposition denoising; wind turbine bearing; weak feature enhancement +
Footnote †: journal: Applied  
amplitude of impulsive feature would be further weakened. Thus, the impulsive feature is often masked by the external noises and the unknown vibration sources [4]. Owing to these reasons, it is a great challenge to identify the incipient defect of wind turbine bearing. Aim at this problem, a lot of diagnosis methods have been utilized for processing the bearing incipient defect signal, such as wigner-ville distribution (WVD), wavelet transform (WT), empirical mode decomposition (EMD), and spectral kurtosis (SK). And applications of these methods have already got a certain amount of effectiveness [5]. Nevertheless, some inherent weaknesses of these methods also impair their performances on fault feature extraction. For instance, the cross-term interference in WVD is difficult to avoid when processing mixed signal [6]. WT doesn't possess adaptive signal decomposition ability because mother wavelet function and decomposition level need to be predefined in advance [7]. EMD has the unresolved problems of noise sensibility, endpoint divergence and aliasing effect, which may cause the obtained intrinsic mode function components losing specific meanings [8]. As for SK, the obtained parameters for filter design may be unreasonable due to random impact interference, and the pass-band of constructed filter may not cover the whole resonant region by reason of fixed tiling pattern [9]. Considering the spreading effect of complicated transmission path of unknown time-variant system, the blind deconvolution operation is investigated to recover the impulsive features from the acquired signal.