Fault diagnosis method of rotating machinery based on SILPDA

Aiming at the difficulty of fault classification caused by "dimension disaster" in fault feature sets, a dimension reduction algorithm for fault feature sets based on strengthened intrinsic local preserving discriminant analysis (SILPDA) was proposed. The algorithm integrated the enhanced multi manifold intrinsic model and local similarity matrix into the construction of the objective function. During this period, the multi manifold structure characteristics of the data set were fully considered and the local structure information of the sample was retained, so that the low dimensional feature subset after dimensionality reduction is easy to implement classification operation, and then achieve the effect of improving the accuracy of fault identification. The performance of the algorithm was verified by using the original fault feature set constructed from the vibration signal set of a rotor test-bed. The results show that the algorithm can extract sensitive feature subsets that are conducive to the implementation of classification operation from the original fault data set. These feature subsets will make the boundary between different fault categories clearer. Finally, compared with locality preserving projections ( LPP ) , linear discriminant analysis ( LDA ) , locality margin discriminant projection ( LMDP ) and other algorithms, the algorithm can achieve better fault identification effect. For improving the value density of rotating machinery big data resources, this algorithm provides a theoretical basis for optimizing the data structure model. © 2023 Chinese Vibration Engineering Society. All rights reserved.