Application of Time Domain Manifold Feature Enhancing in Fault Diagnosis of CNC Machine Tool Bearings
Based on the time domain vibration signals of CNC machine tool bearings,a feature enhancement method based on manifold learning is proposed.The time series of collected signals are reconstructed in phase space,and the information entro-py of different sub-manifolds is calculated to construct the representation of the original signal in the feature space.The mani-fold distance in the feature space is used as a measure of different types of faults in the original signal set.By using the Isomet-ric Feature Mapping(ISOMAP)algorithm,while retaining the information of fault types,the isomorphic low-dimensional manifolds of signals in the feature space are obtained for fault type classification.Through the verification analysis of example data sets,it is shown that the information entropy-ISOMAP transformation can express and enhance the type features of bear-ing faults in the low-dimensional feature space,and can be effectively applied to diagnose single and compound fault scenarios of CNC machine tool bearings.
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