Kernel scatter-difference-based discriminant analysis for fault diagnosis

One fundamental problem with the kernel Fisher discriminant analysis (KFDA) for fault diagnosis, is the singularity problem of the within-class scatter matrix due to the small sample size. In this paper, a kernel scatter-difference-based discriminant analysis (KSDA) method is proposed for fault diagnosis. The proposed method can not only produce nonlinear discriminant features of the process data, but also avoid the singularity problem of the within-class scatter matrix. Experimental results are given to show the effectiveness of the new method.