Improved Multi-Domain Feature Extraction Method and Bearing Fault Diagnosis Based on UMAP
In order to solve the problem that the traditional multi-domain feature extraction method occu-pies too much computing resources and has insufficient classification accuracy,a multi domain feature ex-traction method is proposed based on unified manifold approximation and projection algorithm(UMAP).By combining the multi domain feature collection of the original signal with the global information extrac-tion capability of UMAP,information fusion and low dimensional mapping are performed to reconstruct the feature set;On this basis,the feature set is input into the support vector machine for model training to a-chieve bearing fault recognition and diagnosis.Based on the publicly available experimental dataset of roll-ing bearings at western reserve university in the united states,several typical optimization algorithms and traditional multi domain feature extraction methods were compared and analyzed.It was proved that the success rate of the proposed method in identifying rolling bearing fault states was 100%,and the superiority of this method was verified.
fault diagnosismulti-domain feature extractionuniform manifold approximation and projec-tionsupport vector machine
NETL
NSTL
万方数据
维普
