Detection and diagnosis of oscillations in process control by fast adaptive chirp mode decomposition. (April 2020) - Chen, Qiming; Chen, Junghui; Lang, Xun; Xie, Lei; Lu, Shan; Su, Hongye
Abstract: Even though several algorithms have been proposed in the literature for oscillation detection and diagnosis, they can work reliably only for a specific type of oscillation and there is a lack of a common framework that accommodates the detection and diagnosis for various types of oscillations. To tackle this problem, an FACMD-based (fast adaptive chirp mode decomposition) detection and diagnosis framework is established in this study. It consists of two common oscillation detection indices and a novel strategy for diagnosing nonlinear and linear oscillations. Apart from detecting and diagnosing various single/multiple oscillations in single-input single-output (SISO) loop, FACMD can also distinguish the combination of linear or nonlinear oscillations and contribute to the root cause analysis for plant-wide oscillations. Finally, a series of simulations and industrial cases are used for testing. Compared with the existing work, the proposed methodology has better detection and diagnosis accuracy and a higher level of automation, especially in processing complex multiple oscillations. Highlights: A fast adaptive chirp mode decomposition (FACMD) is proposed. A FACMD-based framework for oscillation detection and diagnosis is established. The framework can detect & diagnose single/multiple oscillations in feedback loops. The root cause analysis for unit-wide oscillations can be done under the framework. Comparative studies in simulations and industrial cases show FACMD's advantages.