Fault reconstruction algorithm based on fault-relevant KPCA

In this paper, a fault reconstruction algorithm based on fault-relevant KPCA is proposed. Compared with the traditional fault reconstruction method whose fault model is composed of the first major distribution directions, the proposed reconstruction algorithm gives a deep analysis of the original fault space according to the relationships with normal process information to extract the principal directions that are relevant to, or affected by fault. Considering the nonlinear situation, kernel PCA is applied. Simulation results on the penicillin fermentation process demonstrate the effectiveness of the proposed algorithm.