Multi-Feature Complex Process Monitoring Method Based on Partial Least Squares

As a typical multivariate statistical analysis method, the partial least squares (PLS) has been widely applied in the anomaly monitoring of key performance indicators. However, the data in complex industrial processes may show dynamic or nonlinear characteristics, or both, so the linear model based on PLS is not suitable for this process and may increase the false alarm rate. Therefore, a multi-feature extraction algorithm based on PLS was proposed. Firstly, the dynamic features were extracted based on the dynamic internal model of PLS to obtain the quality-related dynamic subspace and dynamic residual subspace. The PLS regression modeling was carried out for the dynamic residual subspace to further separate the quality related features of static information. Then, the residual subspace was projected to a high dimension to construct a nonlinear PLS model to extract the nonlinear characteristics of variables. Finally, the statistics was constructed in each feature space, and a complete multi-feature hybrid system process monitoring strategy was designed. The example analysis results of Tennessee-Eastman process show that the proposed method has faster fault detection speed and can achieve better fault detection effect.