Rotating machine fault diagnosis using dimension reduction with linear local tangent space alignment

Abstract A novel fault diagnosis method using dimension reduction with linear local tangent space alignment is proposed in this paper. With this method, the mixed-domain feature set is first constructed to completely characterize the property of each fault by combining Empirical Mode Decomposition (EMD) with the Autoregression (AR) model coefficients. Then, Linear Local Tangent Space Alignment (LLTSA) is used to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination. By using the tangent space in the neighborhood of a data point to represent the local geometry, and aligning those local tangent spaces in the low-dimensional space (which is linearly mapped from the raw high-dimensional space), LLTSA can not only gain a perfect approximation of low-dimensional intrinsic geometric structure within the high-dimensional observation data, but can also enhance local within-class relations. Finally, the Littlewoods-Paley wavelet support vector machine (LPWSVM) is proposed to perform fault classification with the obtained low-dimensional eigenvectors. Compared with the existing methods, the proposed approach has improved the fault diagnosis precision. The experiments on deep groove ball bearings fault diagnosis demonstrated the advantage and effectiveness of the proposed fault diagnosis approach.