Fault diagnosis of rotating machine by isometric feature mapping

Principal component analysis (PCA) and linear discriminate analysis (LDA) are well-known linear dimensionality reductions for fault classification. However, since they are linear methods, they perform not well for high-dimensional data that has the nonlinear geometric structure. As kernel extension of PCA, Kernel PCA is used for nonlinear fault classification. However, the performance of Kernel PCA largely depends on its kernel function which can only be empirically selected from finite candidates. Thus, a novel rotating machine fault diagnosis approach based on geometrically motivated nonlinear dimensionality reduction named isometric feature mapping (Isomap) is proposed. The approach can effectively extract the intrinsic nonlinear manifold features embedded in high-dimensional fault data sets. Experimental results with rotor and rolling bearing data show that the proposed approach overcomes the flaw of conventional fault pattern recognition approaches and obviously improves the fault classification performance.