# Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD
###### Abstract
As the premier deconvolution technology, minimum entropy deconvolution (MED) has been successfully utilized for bearing defect identification [10], but its performance is weakened due to it preferably recovers a large random impact rather than the periodic impacts [11]. Subsequently, maximum correlated kurtosis deconvolution (MCKD) [12] technology is further developed to overcome the drawback of MED, and satisfactory diagnosis results have been achieved in some cases [13; 14]. However, some inherent disadvantages also limit its engineering application. For instance, the correlated kurtosis indicator is proposed empirically without researching its statistical properties. In additional, the resample operation needs to be carried out when the deconvolution period isn't integer, which is easy to cause misdiagnosis. Recently, a novel cyclostationary blind deconvolution (CYCBD) approach, which is able to eliminate the influence of vibration transmission path and separate the fault source effectively, is investigated by defining a simple and effective indicator of higher-order cyclostationarity maximization [15]. Nevertheless, the performance of CYCBD is mostly depended on the cyclic frequency and the filter length parameters, which also need to be artificially selected in advance. Thus, the important problem of applying CYCBD is how to set these two parameters appropriately for the best performance. In order to acquire the optimal deconvolution result automatically, the fast spectral coherence is fused with the equal step size search strategy for parameter optimization, and an optimized cyclostationary blind deconvolution (OCYCBD) method is put forward in this paper, whose superior performance on weak fault feature extraction is given expectation. Teager Kaiser energy operator is a non-stationary signal processing technique, which has been widely utilized for tracking and enhancing the transient impact due to favorable time resolution and high computational efficiency [16]. However, it is susceptible to the background noises and become invalid under the condition of severe interferences [17]. Recently, the frequency weighted energy operator (FWEO) based on envelope-derivative operation is investigated as an alternative technology to track the instantaneous energy of a given signal [18]. It has been verified that this technology is able to amplify the impact feature without any preprocessing and exhibits better performance and higher robustness in the presence of background noises and multiple interferences [19]. As an effective signal component separation approach, singular value decomposition (SVD) is suitable for bearing fault signal denoising due to its superiorities of easy implementation and few control variables. The Hankel matrix containing the fault signature and the noise interference can be decomposed into different subspaces corresponding to the singular value sequence. And the signal denoising operation can be implemented by simply reconstructing the matrix with a number of larger singular values. This - admirable trait of SVD is a benefit to enhancing fault feature during bearing injury judgment [20]. Inspired by the respective advantages of the frequency weighted energy operator and the singular value decomposition, the instantaneous energy signal obtained by FWEO operation is further purified by singular value decomposition denoising (SVDD), whose performance on noise interference suppression and fault feature enhancement is promising. Based on the above discussion, for the sake of dealing with the weak defect identification problem of wind turbine bearing, an effective feature extraction and enhancement method combining OCYCBD with SVDD is put forward in this paper. Both the experimental signal and the actual engineering case are applied to verify this method. The chapters of this paper are structured as follows. The theoretical background of CYCBD, the influences of key parameters, and the parameter optimization strategy of OCYCBD are introduced in Section 2.