Manifold Sensing-Based Convolution Sparse Self-Learning for Defective Bearing Morphological Feature Extraction

TL;DR: In this article, a convolution sparse self-learning (CSSL) is proposed to accomplish an adaptive feature enhancement for bearing fault diagnosis in an intelligent industry, where the representation for desired morphological structures is promoted by a two-dimensional optimizing approach with manifold sensing.

Abstract: The transient features caused by a local fault are of vital importance for bearing fault diagnosis in an intelligent industry. Due to the uncertainty of fault forms and nonstationarity of operating conditions, the fault feature distribution influenced by the physical dynamic response of actual defect is always complex and irregular with morphological differences. This will bring embarrassments for an accurate fault diagnosis. Motivated by this, a convolution sparse self-learning (CSSL) is proposed in this article to accomplish an adaptive feature enhancement. In the view of image sparse processing, the representation for desired morphological structures is promoted by a two-dimensional optimizing approach with manifold sensing. From a randomly selected fragment, the time–frequency manifold learning is first applied to mine the latent structures. The image entropy is then introduced to adaptively output the optimal one as a sensing kernel. Therewith, this kernel is used to operate a shift-invariant sparse analysis on raw time–frequency image. Combining this rebuilt image with the raw phase, an enhanced signal is finally synthesized. In this manner, the desired transient morphology can be automatically mined, which is consistent with the physical dynamic response. Practical defective bearing data are analyzed to illustrate the effectiveness of the proposed method. Specifically, a comparison further illustrates that the proposed CSSL is superior in the morphological transient features enhancement.