Improved Alarm Visualization System Based on Two-Dimensional Online Root-cause Contribution Graph for Industry 4.0

摘要

Process industry has entered a period of rapid development, and industrial safety is an eternal theme of industry 4.0. With the development of industry 4.0, the generation of a large amount of process data provides a research basis for process monitoring. The data-driven process monitoring algorithm sharply finds the abnormal value of the process by analyzing the data of normal condition. Different from the traditional alarm method and principal component analysis (PCA) contribution graph method, the Two-Dimensional Online Root-cause Contribution Graph (2D-ORCG) proposed in this paper is based on the traditional PCA method. Capturing information by the online root-cause variable visualization strategy proposed in this paper. Contribution analysis based on dynamic data can more effectively capture the key variables that affect the stability of the system, and also can accurately capture different variables at different times. Comparing to the traditional Two-Dimensional Contribution Graph method, the effectiveness of the 2D-ORCG method is demonstrated on the Tennessee Eastman Process (TEP).