MPCA-based train suspension system fault analysis method and system

The invention discloses an MPCA-based train suspension system fault analysis method. According to the method disclosed in the invention, a multilinear principal component analysis method (MPCA) is applied to the fault diagnosis of railway vehicle suspension systems. In order to find the weak faults of the suspension systems conveniently and acquire fault information as much as possible, acquired original two-dimensional data is constructed into a third-order tensor form, the advantages, of processing the tensor data, of the MPCA is utilized to decrease the variable and temporal correlation in a local neighborhood as far as possible, and dimension reduction processing and characteristic extraction are carried out on training samples (regarded as tensor objects) in a plurality of (mode) directions, so that the structure and dependency of the original data are protected. Each sample is expressed by using an information amount which is least and has most remarkable characteristics to the greatest extent, so that