Detecting Process State Changes by Nonlinear Blind Source Separation

A variant of nonlinear blind source separation, the Nonlinear Dynamic Factor Analysis (NDFA) model, is based on noisy nonlinear mixtures of state variables, which are controlled by nonlinear system dynamics. The problem setting is blind because both the state variables, the nonlinear mixing model, and the nonlinear dynamics are unknown. As a special problem we consider the ability of NDFA to detect abrupt changes in the process dynamics. It is shown experimentally that NDFA is highly accurate and outperforms several standard change detection methods in this task.