New decision rules for Fisher discriminant analysis : applied to fault diagnosis

A novel framework for fault diagnosis is proposed. New rules are presented to enhance decision making under a probabilistic latent model. The proposed decision rules improve Fisher discriminant analysis-based scheme for fault diagnosis. They allow it to deal with known and not acknowledged faults. Performance evaluation using the Tennessee Eastman Process data is presented and shows good results in comparison to the state of the art.