Aero-Engine Wear Fault Diagnosis with Supervised Locally Tangent Space Alignment

To solve the problem that the traditional feature extraction technique is hard to deal with the complex fault data containing nonlinear structure, an aero-engine fault diagnosis method based on supervised locally tangent space alignment is proposed by introducing the non-linear dimensionality reduction method named locally tangent space alignment algorithm. The method learns the intrinsic geometric features of fault manifold data, and non-linearly maps them into a low-dimensional fault feature space to achieve fault feature extraction. The wear fault recognition and diagnosis are carried out in the fault feature space by constructing classifier. The oil spectra data of engine wear fault are used for experiment. Compared with the conventional feature extraction approaches such as PCA and LDA, the proposed approach can effectively extract the nonlinear features embedded in the fault data to improve the fault classification accuracy and provide good fault diagnosis performance with a simple linear classifier. © 2020, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.