Preload state detection for precision spindle bearings based on multi-level classification

In this study, a multi-level state classification method based on support vector data description (SVDD) is proposed to detect bearing preload state. Firstly, a three-category classification support vector data description algorithm is proposed to establish the three-state non-aliasing hypersphere model, which can combine the kernel principal component analysis (KPCA) and membership degree. Then, by classifying the first-level training sample model of uniform and non-uniform preload states, the second-level training sample model of non-uniform preload states is established based on SVDD algorithm. Moreover, the balanced multi-label propagation classification criterion is defined that can be used to identify the preload state level based on the training sample model. Finally, a preload state detection system is developed, which can accurately simulate uniform/non-uniform preload states for spindle bearings. The experimental results demonstrate that the proposed algorithm can effectively classify the preload states of spindle bearings with average accuracy higher than 94%.