Research on Method of Process Monitoring with Deterministic Disturbances Based on Just-in-Time Learning

Abstract

An industrial process system plays a crucial role in the economic development of a country or region, process monitoring is effective in ensuring the safety and reliability of industrial processes, and has received much attention. For complex nonlinear systems, the traditional model-based methods and knowledge-based methods are difficult to apply, and data-driven methods provide a new solution. However, for the complex nonlinear systems with deterministic disturbances, the existing data-driven approaches also exhibit defects because they no longer satisfy the Gauss distribution. To solve this problem, a method called JITL-DD for process monitoring of nonlinear systems with deterministic disturbances is proposed. The JITL-DD combines the JITL model and the DD fault diagnosis method, the JITL model is used to predict the output of the local model, then the residual is processed as the input of the DD, and the fault information is obtained by analyzing the residual. The continuous stirred tank heater process is used as a simulation of the nonlinear system to illustrate the effectiveness of the proposed method.