关键词: 非参数特征加权, 模糊核聚类, 自适应聚类数, 旋转机械, 故障诊断

Abstract: To improve the accuracy of fault identification,a novel algorithm which could adaptively determine the clustering number of Nonparametric Weighted Feature Extraction (NWFE) and Kernel-based Fuzzy C-means (KFCM) was put forward.The kernel function was combined with fuzzy C-means and the weighted clustering center in NWFE algorithm was adopted to assign different weights for each sample.Besides,clustering evaluation index PBMF was introduced to adaptively determine the optimal clustering number.The performance of algorithm was verified via classical data sets and experiment.The results showed that the disadvantages of classical algorithm were eliminated and the proposed algorithm outperformed the classical algorithm in clustering analysis of rotor failure data sets.

Key words: nonparametric weighted feature extraction, kernel-based fuzzy C-means, adaptive determine the clustering number, rotating machinery, fault diagnosis