Detecting oscillations via adaptive chirp mode decomposition
Published in CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) 2019, 2019
Recommended citation: Qiming Chen, Xun Lang, Lei Xie, Hongye Su. CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS). (2019). https://ieeexplore.ieee.org/document/9213346
High performance is a fundamental requirement for the maintenance of an industrial plant in face of increasing competitive pressure. Oscillation is one of the most common abnormal phenomena encountered in process industries, thus it is of great importance to detect oscillations before implementing the performance-improvement methods. This paper proposed a novel method based on the adaptive chirp mode decomposition (ACMD) to detect the oscillations in control loops. Firstly, the process variable is decomposed by the ACMD into several chirp modes, called as intrinsic mode functions (IMF). Then, the energy ratio, normalized correlation coefficient, consistency function and sparseness index are combined to identify oscillations contained in these IMFs. The proposed method is automatic and data-driven without requiring any prior knowledge about the underlying process dynamics. Simulation studies demonstrated the detection ability of our approach in five cases, i.e. normal condition, external disturbance, external disturbance and poor tuning, stiction, stiction with external disturbance. Results obtained from this approach on industrial data show that it can be readily implemented in industrial environment.