Online Steady-State Detection for Process Control Using Multiple Change-Point Models and Particle Filters

Steady-state detection is critical in process performance assessment, fault detection, and process automation and control. We proposed a robust online steady-state detection algorithm using multiple change-point model and particle filtering techniques. The steady-state detection problem is formulated as a multiple change-point problem using a segmented linear model. A particle filtering algorithm with stratified importance sampling and partial Gibbs move is developed to estimate this model. A generic timeliness improvement strategy is proposed to reduce the detection delay. Extensive numerical analysis shows that the proposed method is more accurate and robust than the other existing methods.