Bayesian methods for control loop diagnosis in the presence of temporal dependent evidences

Conventional Bayesian methods commonly assume that the evidences are temporally independent. This condition, however, does not hold for most engineering problems. With the evidence transition information being considered, the temporal information can be synthesized within the Bayesian framework to improve diagnosis performance. In this paper, the important evidence dependency problem is solved by a data-driven Bayesian approach with consideration of evidence transition probability. The applications in a simulated distillation column and a pilot scale process are presented to demonstrate the data dependency handling ability of the proposed control loop performance diagnosis approach.