Abstract
To realize automation and high accuracy of pedestal looseness extent recognition for rotating machinery, a novel pedestal looseness extent recognition method for rotating machinery based on vibration sensitive time-frequency feature and manifold learning dimension reduction is proposed. Firstly, the pedestal looseness extent of rotating machinery is characterized by vibration signal of rotating machinery and its spectrum, then the time-frequency features are extracted from vibration signal to construct the origin looseness extent feature set. Secondly, the algorithm of looseness sensitivity index is designed to filter out the non-sensitive feature and poor sensitivity feature from the origin looseness extent feature set, avoiding the interference of non-sensitive and poor sensitivity feature. The sensitive features are selected to construct the looseness extent sensitive feature set, which has stronger characterization capabilities than the origin looseness extent feature set. Moreover, an effective manifold learning method called linear local tangent space alignment (LLTSA) is introduced to compress the looseness extent sensitive feature set into the low-dimensional looseness extent sensitive feature set. Finally, the low-dimensional looseness extent sensitive feature set is inputted into weight K nearest neighbor classifier (WKNNC) to recognize the different pedestal looseness extents of rotating machinery, the WKNNC’s recognition accuracy is more stable compared with that of a k nearest neighbor classification (KNNC). At the same time, the pedestal looseness extent recognition of rotating machinery is realized. The feasibility and validity of the present method are verified by successful pedestal looseness extent recognition application in a rotating machinery.
Highlights
The pedestal looseness often occurs in rotating machinery due to the poor quality of installation or long time vibration, such as wind turbine, tunnel fan, electric machinery, rolling mill and so on
In order to compare the dimension reduction and redundant treatment effect of PCA, the LPP, the LDA, and the local tangent space alignment (LLTSA) method are used to reduce the dimension for origin looseness extent feature set
A pedestal looseness extent recognition method for rotating machinery based on vibration sensitive time-frequency feature and manifold learning has been proposed in this paper
Summary
The pedestal looseness often occurs in rotating machinery due to the poor quality of installation or long time vibration, such as wind turbine, tunnel fan, electric machinery, rolling mill and so on. The low-dimensional feature set is inputted into a learning machine for pattern recognition, for example, K nearest neighbor classifier (KNNC) [14, 15], artificial neural network (ANN) [16, 17] and sup-port vector machine (SVM) [18, 19] Application of this fault recognition method for looseness extent of fan foundation will face the following problems: 1) loose-ness feature extraction, and non-sensitive or poor sensitive feature interference. The traditional dimension reduction method can effectively re-duce the linear high dimensional feature set, such as PCA, LPP, LDA and so on, these dimension reduction methods have limited effect on the reduction of the non-linear feature set for looseness of fan foundation To handle these problems and realize automation and high accuracy of pedestal looseness extent recognition for rotating machinery, a pedestal looseness extent recognition method for rotating machinery based on vibration sensitive time-frequency feature and manifold learning is proposed. The extraction method of pedestal looseness extent feature and construction method of pedestal looseness sensitive feature set based on vibration signal
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