Fault diagnosis using locality sensitive discriminant analysis for feature extraction

Extracting the most sensitive and effective characteristics from original high dimensional feature space is significant for accurately identifying the condition of machinery. This paper presents a locality sensitive discriminant analysis (LSDA) based feature dimensionality reduction approach. Different from locally preserving projection (LPP) that only preserves the local structure of data manifold, LSDA can also employ the discriminant information at each local area when conducting the projection. In the proposed fault diagnosis approach, various multi-domain features are calculated aimed at exploring the machinery condition information roundly from different perspectives. LSDA is applied to discover the representative information contained in the high dimensional feature set. Then a lower informative feature space can be constructed with LSDA. Finally, the effective KNN classifier is used to recognize the condition of machinery with the most representative eigenvectors. A bearing fault experiment is conducted to verify the proposed method. The comparison study with PCA, LPP and KLPP indicates that LSDA has advantages in dimensionality reduction and the proposed fault diagnosis approach is effective.