Group-Based K-SVD Denoising for Bearing Fault Diagnosis

Vibration signals acquired by accelerometers are commonly used in the field of bearing fault diagnosis. The measured vibration signals contain a series of impulse responses when localized bearing faults occur. These impulses are often viewed as fault features and exhibit nonlocal self-similarity in terms of morphological characteristics. However, the impulses are generally contaminated by background noise. In this paper, a group-based K-SVD denoising algorithm, which exploits the nonlocal self-similarity property, is presented to extract bearing fault features. In contrast to regular K-SVD denoising, this denoising algorithm forms groups by clustering similar signal segments and regards each group rather than each segment as the basic unit of sparse representation. To group similar segments, we further propose a particular grouping method that exploits bearing prior knowledge (i.e., possible impulse spacings). The new grouping method could form groups in a targeted manner, thereby achieving more accurate grouping. Group-based K-SVD denoising is able to extract fault features better as similar segments collaboratively provide a large amount of shared feature information, especially in the presence of strong noise (such as signal-to-noise ratios lower than -10 dB). The numerical and experimental results demonstrate the superior performance of group-based K-SVD denoising over regular K-SVD denoising and other two benchmark algorithms (i.e., wavelet shrinkage and spectral kurtosis).