National Scientific and Technological Support Projects of China under Grant No.2012BAF12B08
Detection of rolling bearings is very crucial for the reliable operation in the process of condition monitoring of rotating machinery. In this paper, a novel monitoring method using support vector data description (SVDD) with principal component analysis (PCA) for fault diagnosis of corrosion pitting on the raceways and balls in rolling bearings is proposed to improve diagnostic accuracy based on feature extraction dataset of vibration signals. The feasibility and validity of the proposed monitoring scheme are investigated through case study. Experiment results show that the proposed method can achieve 92.85% accuracy, 93.11% sensitivity, and 90.47% specificity based on an unbalanced dataset.