Fault diagnosis based on variable-weighted kernel Fisher discriminant analysis

In this paper, a new fault diagnosis approach with variable-weighted kernel Fisher discriminant analysis (VW-KFDA) is proposed. The approach incorporates the variable weighting into KFDA. The variable weighting finds out the weight vector of each fault by maximizing separation between the normal and each fault data. With continuous non-negative values, each element of the weight vector represents the corresponding variable's contribution to a special fault. After all fault data are weighted by the corresponding weight vectors, KFDA is performed on these weighted fault data. These weight vectors offer important supplemental classification information to KFDA and effectively improve its multi-classification performance. The proposed approach is applied to the Tennessee Eastman process (TEP). The results show superior capability for fault diagnosis to KFDA and FDA.