Fault diagnosis based on imbalance modified kernel Fisher discriminant analysis

Process data with imbalance class distribution has brought a significant drawback to most existing pattern recognition based fault diagnosis algorithms, which have assumed that the process data have an equal misclassification cost and relatively balanced class distribution. The frequent occurrence of the imbalance problem in real industrial process indicates the need for extra research efforts. In this paper, three novel imbalance modified kernel Fisher discriminant analysis (IM-KFDA) approaches are proposed to handle this problem. Two sample-level approaches, over-sampling KFDA and under-sampling KFDA, are presented along with proper stochastic sampling strategies. One algorithm-level approach, inductive bias KFDA, is also proposed with incorporating a novel regular weighted matrix (RWM) into the minimum Euclid distance based pattern classification rule. To improve the fault diagnosis performance, model updating modes for the sample-level and algorithm-level approaches are described, respectively. A simulation case study of Tennessee Eastman (TE) process is conducted to evaluate the proposed fault diagnosis approaches.