Principal component analysis for process monitoring in distributed system environment

Abstract

In modern industrial production processes, process monitoring plays a major role in improving process efficiency and quality of product, and it is an important task in industrial process. The best way to implement process monitoring is to develop models that describe different phenomena in physics, chemistry, and processes. However, because the modeling of industrial processes is often very complex and has significant intrinsic nonlinearities, a reasonable theoretical modeling method are often impractical. With the adoption of distributed control systems, the application of multivariate statistical monitoring is becoming more and more popular in the distributed system environment. This paper uses the technology for multivariate monitoring of continuous production processes. Based on principal component analysis (PCA) and making full use of system statistics information, the method can well deal with the nonlinear and multimode distribution of industrial data, which is difficult to be dealt with by traditional methods. This method can be used not only in process detection but also in fault detection. Finally, the method is applied to the actual example and the Tennessee-Eastman model; the simulation results prove the feasibility of the proposed method and achieve better results.

Research on the Current Situation of Employment Mobility and Retention Rate Predictions of “Double First-Class” University Graduates Based on the Random Forest and BP Neural Network Models

“…This paper begins by employing a principal component analysis (PCA) to reduce the dimensionality of multiple data sets. A principal component analysis is a multivariate statistical analysis technique that replaces the original variable with a linear combination of the original variables to form an uncorrelated comprehensive variable on the premise of preserving the original variable's information with the least possible loss [16,17]. This eliminates the correlation between the original variables, reduces the network dimension, and facilitates data sorting and calculation [18,19].…”

Fault Diagnosis of an Industrial Chemical Process using Machine Learning Algorithms: Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA)

“…Principal component analysis is a popular multivariate statistical technique for process monitoring in business. PCA method is a linear transformation that projects high dimensional, noisy, and correlated data into a lower dimensional subspace [7] [8]. Most industrial processes exhibit nonlinear properties, and PCA may overlook crucial data in nonlinear system behaviour.…”