摘要
In order to improve the accuracy of escalator sprocket bearing fault diagnosis,the problem of the feature extraction method of bearing vibration signal is addressed.In this paper,empirical mode is used to decompose the original signal,and the optimal modal component among the multiple modal components is obtained after the optimization decomposition is selected by the envelope spectrum method,and the multi-angle feature measure is introduced to extract the fault characteristic value.According to the vibration characteristics of the bearing vibration signal data,a bearing signal feature group that is more inclined to the fault feature category information is established,which avoids the absolute problem of extracting a single metric feature.The fuzzy C-means clustering algorithm is used to cluster the sample data with similar characteristics into the same cluster area,which effectively solves the problem that a single measurement analysis cannot characterize the complex internal characteristics ofthe bearing vibration signal.
