A robust discriminate analysis method for process fault diagnosis

A robust Fisher discriminant analysis (FDA) strategy is proposed for process fault diagnosis. The performance of FDA based fault diagnosis procedures could deteriorate with the violation of the assumptions made during conventional FDA. The consequence is a reduction in accuracy of the model and efficiency of the method, with the increase of the rate of misclassification. In the proposed approach, an M-estimate winsorization method is applied to the transformed data set; this procedure eliminates the effects of outliers in the training data set, while retaining the multivariate structure of the data. The proposed approach increases the accuracy of the model when the training data is corrupted by anomalous outliers and improves the performance of the FDA based diagnosis by decreasing the misclassification rate. The performance of the proposed method is evaluated using a multipurpose chemical engineering pilot-facility.