A novel dimensionality reduction and feature extraction method based on manifold learning, locally preserving
principal component analysis(LPPCA) is proposed. In order to overcome the defects that the traditional PCA can only keep
the structure in global and can not maintain the manifold structure in local, the idea of locality preserving is incorporated
into the optimization goals of the PCA. The fault detection based on LPPCA is researched. The validity and superiority
of the LPPCA are verified by the S-Curve numerical simulation, Swiss-roll surface numerical simulation and TE process
simulation.