LAUSR

A class-incremental scheme of fisher discriminant analysis is proposed to improve the performance ofprocess fault diagnosis. Fisher discriminant analysis seeks directions which are efficient for discrimination and has excellent fault detection and diagnostic performance for the sample set with the tag. However, due to the property of the model, it has no detection and diagnostic capabilities forun-seen faults. In order to address this issue, the $F$ direction, which is based on a partial $F$ -values with the principle component analysis, is proposed in this paper. After a new fault being detected and added into the known fault collection, a class-incremental scheme is used to update the fisher discriminant analysis model to enhance the model’s ability for continuous fault identification. The proposed approach is validated by the Tennessee Eastman process for the fault diagnosis. The results demonstrate that the proposed class-incremental fisher discriminant analysis method outperforms other conventional fisher discriminant analysis methods.