关键词: 时、频域特征集|判别式正交线性局部切空间排列|特征约简|流形学习|故障辨识

Abstract: Aiming at the crucial problem that the current prevailing fault diagnosis model was difficult to realize the automation, high-precision and generalization, a novel fault diagnosis method based on feature compression with Discriminant Orthogonal Linear Local Tangent Space Alignment (DOLLTSA) was proposed. With this method, the time-frequency domain feature set was constructed to completely characterize the property of each fault. DOLLTSA was introduced to automatically compress the high-dimensional time-frequency domain feature sets into the low-dimensional eigenvectors with better discrimination, which were input into K-Nearest Neighbors Classifier (KNNC) to carry out fault identification. The time-frequency domain feature set could comprehensively and accurately reflect the fault features of rotating machinery. DOLLTSA not only combined the local geometry with class information for manifold decoupling, but also used spectral regression and subspace orthonormalization approach for solving the optimal low dimensional embedding subspace, which could improve fault identification accuracy. Fault diagnosis example of deep groove ball bearings and life state identification example of space bearing demonstrated the effectiveness of proposed method.

Key words: time-frequency domain feature set|discriminant orthogonal linear local tangent space alignment|feature compression|manifold learning|fault identification