Feature Extraction of Time Series Signal Path Signature and Its Application in Fault Diagnosis
The vibration signal of rolling bearing presents nonlinear and non-stationary characteristics.In order to fully exploit the effective information of the vibration signal of rolling bearings and improve the ac-curacy of fault diagnosis,a feature extraction method of rolling bearing fault signal based on path signature(PS)of vibration signal is proposed.Firstly,the one-dimensional fault vibration signal is delayed recon-structed to form a finite-dimensional path space.Secondly,the high-order path integral feature is obtained by multiple iterative integration of the path as the initial feature of the fault vibration signal,and the princi-pal component analysis(PCA)is used to reduce the dimension to obtain the feature that can fully charac-terize the intrinsic information of the fault signal.Finally,the fault features of different fault signals based on path signature constitute the fault feature set,which is input into the support vector machine(SVM)to complete the fault identification and classification.The analysis results show that the accuracy of this meth-od in diagnosing 10 types of faults on public datasets in 99.33%.Compared with other methods,the pro-posed method can quickly and accurately identify different fault types of rolling bearings.