Valve Stiction Detection Method Based on Dynamic Slow Feature Analysis and Hurst Exponent

Abstract

Valve stiction is the most common root of oscillation faults in process control systems, and it can cause the severe deterioration of control performance and system instability, ultimately impacting product quality and process safety. A new method for detecting valve stiction, based on dynamic slow feature analysis (DSFA) and the Hurst exponent, is proposed in this paper. The proposed method first utilizes DSFA to extract slow features (SFs) from the preprocessed and reconstructed data of the controller output and the controlled process variable; then, it calculates the Hurst exponent of the slowest SF to quantify its long-term correlation; and, finally, it defines a new valve detection index to identify valve stiction. The results obtained from simulations and actual process case studies demonstrate that the proposed method, based on a DSFA–Hurst exponent, can effectively detect valve stiction in control loops.