Neuronal Principal Component Analysis for Nonlinear Time-Varying Processes Monitoring

This paper presents a new adaptive kernel principal component analysis algorithm (AKPCA) for nonlinear time-varying process monitoring. The basic idea is to use a neuronal principal component analysis based on the kernel version of the generalized Hebbian algorithm. The proposed algorithm follows a new methodology to update the KPCA model. At each time instant, when a new data is available, the KPCA model is updated accordingly without having to re-explore all previous data. By using the proposed algorithm, the performance of process monitoring is improved in two aspects; the speed computation and adaptation of the KPCA model, and the storage memory complexity. To identify faults in a dynamic process, the reconstruction based contribution approach is used and adapted in real time. The results for applying this algorithm on the Tennessee Eastman process shows its feasibility and advantageous performances.