Nonlinear process fault diagnosis based on slow feature analysis

Invariant features of temporally varying signals are very useful for process monitoring. A novel nonlinear process fault diagnosis method is proposed in this paper based on slow feature analysis (SFA) which is a new invariant learning method. In the proposed method, input-output transformation functions are optimized to extract the nonlinear slowly varying components as invariant features. Based on feature variables, two monitoring statistics are constructed for fault detection and their confidence limits are computed by kernel density estimation. Simulation using a continuous stirred tank reactor (CSTR) system shows that the proposed method outperforms the traditional PCA and KPCA method.