关键词: 多分辨奇异值分解, 变量预测模型模式识别, 故障诊断

Abstract: In view of the problem that the feature components are easy to be submerged in noise at early stage of bearing fault, and the difficulty to obtain a large number of fault samples in practice, a bearing fault intelligent diagnosis method based on Multi-Resolution Singular Value Decomposition（MRSVD） and Variable Predictive Model based Class Discriminate（VPMCD） is put forward. The detail components that contain the fault features are extracted by using the MRSVD for decomposing the bearing acceleration vibration signal. The logarithmic normal distribution models are established to highlight the non Gauss feature of detail components. The feature vector is constructed by calculating logarithmic means and logarithmic standard deviations. It is used to identify the fault by VPMCD. The method is applied to the actual bearing fault diagnosis with local weak faults of outer, inner circle and balls, and the?fault type can be accurately identified with 98.75% accuracy. The result shows that the presented method is feasible and valid.

Key words: Multi-Resolution Singular Value Decomposition（MRSVD）, Variable Predictive Model based Class Discriminate（VPMCD）, fault diagnosis