关键词: 风机基座, 松动程度, 敏感特征, 正交局部保持映射, 加权k最近邻分类器, 故障诊断

Abstract: To realize the automation and high accuracy of looseness extent diagnosis for connecting bolts of fan foundation,and to solve the key problems of looseness extent feature extraction,sensitivity feature selection,nonlinear dimension reduction and looseness extent stability identification,a looseness extent diagnosis method for connecting bolts of fan pedestal based on vibration sensitive feature and manifold learning dimension reduction was proposed.The time-frequency features were extracted from vibration signal to construct the origin looseness extent feature set,and the quantitative characterization for looseness extent of connecting bolts was realized.The calculation method of looseness extent sensitivity index was designed,and the sensitive features were selected to construct the looseness extent sensitive feature set that had stronger characterization capabilities.Moreover,the manifold learning method called Orthogonal Locality Preserving Projection (OLPP) was introduced to compress the looseness extent sensitive feature set into the low-dimensional looseness extent feature set,and the different looseness extents of connecting bolts was recognized by Weight K Nearest Neighbor Classifier (WKNNC).The feasibility and validity of the proposed method were verified by experimental results.

Key words: fan pedestal, looseness extent, sensitive feature, orthogonal locality preserving projection, weight K nearest neighbor classifier, fault diagnosis