基于PNN的发动机PT燃油系统故障诊断

针对传统故障诊断方法的局限性,提出了基于PNN的故障诊断方法,并以发动机的PT燃油系统为例,进行了故障诊断研究;通过与常用的BP神经网络进行对比试验,验证了基于PNN的故障诊断方法的有效性.     

Research of Adaptive Neighborhood Incremental Principal Component Analysis and Locality Preserving Projection Manifold Learning Algorithm

In view of the incremental learning problem of manifold learning algorithm, an adaptive neighborhood incremental principal component analysis (PCA) and locality preserving projection (LPP) manifold learning algorithm is presented, and the incremental learning principle of algorithm is introduced. For incremental sample data, the adjacency and covariance matrices are incrementally updated by the existing samples; then the dimensionality reduction results of the incremental samples are estimated by the dimensionality reduction results of the existing samples; finally, the dimensionality reduction results of the incremental and existing samples are updated by subspace iteration method. The adaptive neighborhood incremental PCA-LPP manifold learning algorithm is applied to processing of gearbox fault signals. The dimensionality reduction results by incremental learning have very small error, compared with those by batch learning. Spatial aggregation of the incremental samples is basically stable, and fault identification rate is increased.