Bearing fault diagnosis based on intrinsic time-scale decomposition and improved Support vector machine model

In order to achieve the bearing fault diagnosis so as to ensure the steadiness of rotating machinery. This article proposed a model based on intrinsic time-scale decomposition (ITD) and improved support vector machine method (ISVM), so as to deal with the non-stationary and nonlinear characteristics of bearing vibration signals. Firstly, the feature extraction method intrinsic time-scale decomposition (ITD) is used and the energy entropy are extracted so as to process the vibration signal in this paper. Then, the local tangent space alignment (LTSA) method is introduced to extract the characteristic features and reduce the dimension of the selected entropy features. Finally, the features are used to train the ISVM model as to classify bearings defects. Cases of actual were analyzed. The results validate the effectiveness of the proposed algorithm.

[1]  Shichang Du,et al.  Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes , 2013 .

[2]  Jun Lv,et al.  Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines , 2013, Comput. Ind. Eng..

[3]  Deli Zhao,et al.  Linear local tangent space alignment and application to face recognition , 2007, Neurocomputing.

[4]  Konstantinos C. Gryllias,et al.  A Support Vector Machine approach based on physical model training for rolling element bearing fault detection in industrial environments , 2012, Eng. Appl. Artif. Intell..

[5]  Robert X. Gao,et al.  PCA-based feature selection scheme for machine defect classification , 2004, IEEE Transactions on Instrumentation and Measurement.

[6]  Theodoros Loutas,et al.  Rolling element bearings diagnostics using the Symbolic Aggregate approXimation , 2015 .

[7]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..

[8]  Shaojiang Dong,et al.  Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation , 2012, Signal Process..

[9]  Shaojiang Dong,et al.  Rotating Machine Fault Diagnosis Based on Optimal Morphological Filter and Local Tangent Space Alignment , 2015 .

[10]  Lei Xie,et al.  Online detection of time-variant oscillations based on improved ITD , 2014 .

[11]  Li Ma,et al.  A roller bearing fault diagnosis method based on the improved ITD and RRVPMCD , 2014 .

[12]  Shaojiang Dong,et al.  Bearing degradation state recognition based on kernel PCA and wavelet kernel SVM , 2015 .

[13]  Shaojiang Dong,et al.  Bearing degradation process prediction based on the PCA and optimized LS-SVM model , 2013 .