Detecting nonlinear interrelation patterns among process variables using genetic programming

Detecting non-linear interaction patterns among process variables is an important task for fault detection and propagation analysis. There are many statistical and evolutionary techniques being developed in the literature for prediction of interaction strengths but their accuracy is generally unsatisfactory. This study demonstrates an evolutionary programming approach to uncover non-linear relations among process variables. In this study, we make an attempt to use genetic programming (GP) based approach for this purpose. GP overcomes many shortcomings faced by other statistical or evolutionary techniques in this context. The effectiveness, feasibility, and robustness of the proposed method are demonstrated on simulated data emanating from a well-known Tennessee Eastman process. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns among process variables.