In this paper, a new data-driven fault detection method based on distributed canonical correlation analysis (D-CCA) is proposed to address the plant-wide process monitoring problem. The paper focuses on the distributed plant-wide processes. The core of the proposed method is to reduce uncertainties using correlation information from the neighboring nodes. Furthermore, the cost of data transmission between network nodes is also reduced by the D-CCA algorithm. When the proposed method and the existing methods are compared using the Tennessee Eastman benchmark process, the false alarm rate, fault detection rate, and the detection delay are comparable. This suggests that the proposed method is feasible.

Projection to latent structures (PLS) model has been widely used in quality-related process monitoring, as it can establish a mapping relationship between process variables and quality index variables. To enhance the adaptivity of PLS, kernel PLS (KPLS) as an advanced version has been proposed for nonlinear processes. In this paper, we discuss a new total kernel PLS (T-KPLS) for nonlinear quality-related process monitoring. The new model divides the input spaces into four parts instead of two parts in KPLS, where an individual subspace is responsible in predicting quality output, and two parts are utilized for monitoring the quality-related variations. In addition, fault detection policy is developed based on the T-KPLS model, which is more well suited for nonlinear quality-related process monitoring. In the case study, a nonlinear numerical case, the typical Tennessee Eastman Process (TEP) and a real industrial hot strip mill process (HSMP) are employed to access the utility of the present scheme.

This paper proposes a common and individual (CnI) feature extraction-based process monitoring (PM) method for tracking the operating performance and product quality of processes with multiple operating modes. Different from traditional methods that separately develop PM models concerning only the individual feature of each mode data, the new method seeks to build the PM model simultaneously from all mode data, including to acquire the common subspace that captures the common feature behind different modes, and the individual subspace that reflects the unique feature of each mode. The newly proposed framework is achieved using the conventional principal component analysis (PCA) and partial least squares (PLS)-based methods. The resulting CnI-PCA-based operating performance monitoring method and CnI-PLS-based product quality monitoring method are applied to the typical multimode finishing mill process (FMP) where common configuration for all steel products and individual setting for each steel are existing. Finally, the practical application result shows that the proposed method can be preferable to detect and identify different faults in the multimode FMP.

In this study, a new statistical monitoring technique based on efficient projection to latent structures (EPLS) is proposed for quality‐relevant fault detection in multivariate processes that exhibit collinear measurements. The algorithm decomposes process variables into three subspaces according to singular value decomposition (SVD) and principal component analysis. This method effectively solves the problems of real‐time and effectiveness in the quality‐relevant fault diagnosis of industrial processes. EPLS takes full advantage of good nature of SVD. As a result, the calculation of the projection process is greatly reduced. It is reasonable that the process data space was projected to three subspaces, a quality‐relevant subspace, a quality‐irrelevant subspace and a residual subspace. EPLS algorithm not only effectively avoids the tedious iterative calculation, but also makes more explicit spatial resolution. The framework of quality‐relevant fault monitoring based on EPLS algorithm was proposed as well in this study. A challenging problem, real industrial hot strip mill process, is used to illustrate the effectiveness of the proposed method.