Dual-kernel driven convolutional sparse learning for bearing transient feature enhancement

Abstract

The transient structure feature of bearing reflects the dynamic response of vibration, which is of vital importance for rotating machinery fault diagnosis. However, the measured signals are complicated and non-stationary, where the transient impacts are always submerged in heavy background noise. In this study, a novel dual-kernel driven convolutional sparse learning (DKCSL) is proposed to enhance the transient feature with the intrinsic structure mined. Considering the benefits of data-driven representation with time–frequency manifold (TFM) learning and model-driven representation with wavelet analysis, two time–frequency kernels with different principles are constructed for 2-dimensional convolutional sparse learning (CSL) in a fusion way of both data-driven and model-driven. From the perspective of image processing, the representation of desired transient structure features can be well sensed from time-frequency distribution (TFD) by those two dual-driven time–frequency kernels. In this manner, the desired defective intrinsic features can be mined, which is consistent with the physical dynamic response. In addition, image entropy is introduced to output the optimal TFM kernel and a series of inverse transform are applied to obtain the enhanced signal waveform. Simulated and experimental data are tested here to verify the creativity and effectiveness of the proposed DKCSL method, and a comparison with the traditional single TFM kernel-based sparse result and wavelet kernel-based sparse result are further provided, which deeply indicates the originality of our method that its potential in intrinsic structure feature extraction with dual-kernel sensing for bearing fault diagnosis.