Detecting and Analyzing Nonlinearity-Caused Oscillations in Process Control Systems Using an Improved VNCMD

Abstract

Nonlinearity-caused oscillations are a frequent issue in process control systems. Its incidence degrades the product quality, stability and safety of the plant. Therefore, it is important to detect and analyze the nonlinearity-caused oscillations to maintain the control performance. In this study, we propose a novel oscillation detection and analyze method based on an improved variational nonlinear chirp mode decomposition (VNCMD) algorithm. Specifically, the original VNCMD needs to manually set the mode number in advance, which is a challenging task in practice. To tackle this problem, an improved VNCMD (IVNCMD) is proposed by utilizing the approximate entropy of instantaneous frequency. Then a novel IVNCMD-based detector is developed to detect and analyze the nonlinearity-induced oscillations by revealing the harmonic content of process variable. Besides detecting the nonlinearity problem, the IVNCMD-based method can contribute in locating the root cause for nonlinearity-caused unit-wide oscillations. The proposed method is model-free and data-driven thus requiring no prior knowledge about the process dynamics. Compared with the latest related methods, the proposed method is able to process nonstationary oscillations and providing corresponding time-frequency information. The effectiveness and advantages are demonstrated through simulations as well as industrial applications.

Keywords

• Nonlinearity-induced oscillation detection
• variational mode decomposition
• signal decomposition
• control performance assessment