Deep stacking l1-norm center configuration convex hull and its application in fault diagnosis of rolling bearing

Abstract

Maximum margin classifier with flexible convex hulls (MMC-FCH) is an adaptive pattern recognition method based on convex hull vector and shrinkage factor, which can effectively identify different fault states. However, MMC-FCH is a shallow learning algorithm that cannot effectively diagnose complex signals. Meanwhile, MMC-FCH is essentially a binary classifier. For multi-classification, MMC-FCH can only perform multiple binary classifications. To overcome the shortcomings of MMC-FCH, we propose a deep stacking center configuration convex hull (DSC3H), which combines the convex hull with the idea of stacking-based representation learning (SRL). In DSC3H, the output of all previous modules combine with the original signal as the input of the next module to fully learn the information in the original signal. At the same time, the concept of center configuration is used to construct the multi-classification objective function of center configuration convex hull (C3H). However, redundant information and noise information may still exist in the proposed DSC3H method. Therefore, we further propose a deep stacking l1-norm center configuration convex hull (DSl1C3H) method, which makes the prediction model more robust and sparser under the constraint of l1-norm distance. The experiments of rolling bearings show that the proposed DSl1C3H method has better classification performance.