关键词: 轴承, 性能退化评估, 独立成分分析, 自组织映射, 贝叶斯推论

Abstract: A bearing performance degradation assessment method based on Bayesian inference and Self-Organizing Map (SOM) was proposed. Independent Component Analysis (ICA) was used to extract feature set of normal operation bearing from original feature set, and a SOM model was established to describe the healthy bearing state space. Furthermore, Negative Log Likelihood Probability (NLLP)-based performance quantification index and Bayesian Inference-based failure probability calculation method were developed to evaluate and identify dynamical running states of bearings online. In comparison with traditional feature indexes and Support Vector Data Description (SVDD) based performance degradation assessment, the experimental results of a bearing test bed showed that the proposed evaluation index was more effective to quantify the performance degradation propagation on the whole life of bearings. It could provide important healthy information for making maintenance plan.

Key words: bearing, performance degradation assessment, independent component analysis, self-organizing map, Bayesian inference, fault diagnosis