Abstract: Partial least squares (PLS) as a typical multivariate statistical analysis method, has been widely used for the monitoring of key performance indicators. However, in the complex industrial processes, the data may exist dynamic, nonlinear features, or both. The linear PLS is not suitable for these cases, and may lead to high false alarm rate and false alarm rate. Therefore, this paper proposes a multi-feature extraction algorithm based on partial least squares for the forementioned complex process. The proposed algorithm extracts dynamic features based on dynamic internal PLS (DiPLS) to obtain quality-related dynamic subspace and dynamic residual subspace; In order to further separate the quality-related features of static information, PLS regression modeling is carried out on dynamic residual subspace; In addition, to extract the nonlinear features of variables, the residual subspace is mapped to a high dimension to construct a nonlinear PLS model; By constructing statistics in each feature space, a complete process monitoring strategy for multi-feature hybrid system is designed. Finally, through the example analysis of Tennessee-Eastman (TE) process, it is verified that the proposed method has faster fault detection speed and can achieve better fault detection effect.

Key words: Partial least squares, Key performance indicators, Multi feature extraction, Process monitoring, Fault detection