Valve stiction detection for closed-loop controllers

A method, comprising:

• receiving a set point (SP) signal, a process variable (PV) signal, and a controller output (CO) signal comprising a multivariate time series of data from a control loop for a control valve of a chemical process;
• wherein the CO signal is received from a controller that controls a position of the control valve;
• transforming the multivariate time series data coming from each of the SP signal, the PV signal and the CO signal utilizing a continuous wavelet transform (CWT) to provide respective CWT coefficients for each of the SP signal, the PV signal and the CO signal;
• assembling the respective CWT coefficients into respective matrices;
• merging the respective matrices to provide a red, green and blue (RGB) image;
• inputting the RGB image into a deep convolutional neural network (CNN) trained to identify a non-linear control valve event in the control loop of the chemical process based on a predetermined feature of the RGB image;
• and
• adjusting a setting of the controller to minimize the non-linear control valve event in response to the CNN identifying the predetermined feature.