Monitoring approach using Nonlinear Principal Component Analysis

This paper presents fault detection and diagnosis based on Neural Non Linear Principal Component Analysis (NNLPCA) and a Partial Least Square (PLS). A new process monitoring method is proposed and is applied to fault detection of a manufacturing process. The performance of the proposed approach is then illustrated and compared to those of classic LPCA.