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.

Slow feature analysis (SFA) has been adopted for control performance monitoring (CPM) recently. However, due to the selection criterion of the dominant slow features (SFs) and the performance monitoring statistics, the traditional SFA-based CPM method has certain limitations in monitoring model predictive control (MPC) performance and fails to distinguish the direction of performance change, i.e., whether the performance becomes better or worse. In order to solve the above problems, an MPC performance monitoring and grading strategy based on improved SFA is proposed in this paper. First, a new criterion for selecting dominant SFs is proposed. On this basis, two combined monitoring indices are built to monitor steady-state and dynamic characteristics of MPC systems, respectively. Besides, an SFA-based predictable performance assessment index is proposed to indicate the direction of performance change. Finally, a performance grading strategy based on improved SFA is established to classify current MPC performance to four levels. Two simulation examples demonstrate the effectiveness and superiority of the proposed method.

Due to the wide application of model predictive control (MPC) in industrial processes, the assessment of MPC performance is essential to ensure product quality and improve energy efficiency. Recently, the slow feature analysis (SFA) algorithm has been successfully applied to assess the performance of MPC. However, the disadvantage of the traditional SFA-based predictable index is that it can only extract one-step predictable information in the monitored variables. In order to better mine the predictable information contained in the monitored variables with large lag, an enhanced method to assess MPC performance based on multi-step SFA (MSSFA) is proposed. Based on the relationship between the slowness of slow features (SFs) and data predictability, an MSSFA model SFA(τ) is built through extending the temporal derivatives of the SFs from one step to multiple steps to extract multi-step predictable information in the monitored variables, which is used to construct a multi-step predictable index. Then, the predictable information in the SFs is further extracted for enhancing the multi-step predictable index to improve its sensitivity to performance changes. The effectiveness of the proposed method has been verified through two process simulation examples.