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.

The research on fault diagnosis based on multi-source information fusion technology aims to comprehensively integrate the diagnostic information of complex mechanical and electrical equipment, providing a scientific and precise decision-making basis for decision-makers. However, in diagnostic practice, issues such as the impact of component replacement, rule combination explosion, and information redundancy have become research difficulties. To address these challenges, this paper innovatively combines equipment mechanisms with expert knowledge to build an optimized model that considers the impact of component replacement based on the traditional Belief Rule Base (BRB-h). Meanwhile, under the framework of traditional independent component analysis, this paper proposes an Independent Component Analysis (ICA) method that considers Expert knowledge (ICA-E). Furthermore, to quantify the impact of component replacement on equipment performance, this paper delves into the transparency and traceability of replacement impact factors and conducts a sensitivity analysis. Through empirical case studies, the advancement and practicability of this new method in the field of fault diagnosis are verified.

Principal/minor component analysis (PCA/MCA), generalized principal/minor component analysis (GPCA/GMCA), and singular value decomposition (SVD) algorithms are important techniques for feature extraction. In the convergence analysis of these algorithms, the deterministic discrete-time (DDT) method can reveal the dynamic behavior of PCA/MCA and GPCA/GMCA algorithms effectively. However, the dynamic behavior of SVD algorithms has not been studied quantitatively because of their special structure. In this paper, for the first time, we utilize the advantages of the DDT method in PCA algorithms analysis to study the dynamics of SVD algorithms. First, taking the cross-coupled Hebbian algorithm as an example, by concatenating the two cross-coupled variables into a single vector, we successfully get a PCA-like DDT system. Second, we analyze the discrete-time dynamic behavior and stability of the PCA-like DDT system in detail based on the DDT method, and obtain the boundedness of the weight vectors and learning rate. Moreover, further discussion shows the universality of the proposed method for analyzing other SVD algorithms. As a result, the proposed method provides a new way to study the dynamical convergence properties of SVD algorithms.