A novel imbalanced fault diagnosis method integrated KLFDA with improved cost-sensitive learning ANBSVM

Fault diagnosis, as an important approach to ensure the safety and stability of industrial processes, has been widely studied in recent years. During the running process, it is noted that the normal data are always much more than the fault data, which demonstrates imbalanced characteristics and leads to a negative effect on the overall accuracy of fault diagnosis. Targeting the problem, a novel imbalanced fault diagnosis method integrated kernel local Fisher discriminant analysis (KLFDA) with improved adaptive near-Bayesian support vector machine (ANBSVM) is proposed in this paper. First, KLFDA is used to extract the non-linear features while maintaining the local spatial structure of the data by introducing flow pattern learning. Second, considering the imbalance characteristics of the data, the data set is divided into a majority class (normal data) and a minority class (fault data). The density distributions of the two classes in their overlapping region are characterized by the proportional function of variance. Third, by minimizing the Bayesian error under the proportion function, the weight factors are adaptively obtained and then introduced into the objective function of the support vector machine (SVM). Namely, a cost sensitivity-based ANBSVM classifier for fault diagnosis is constructed. Finally, by the simulation experiment on the Tennessee Eastman (TE) process, the comparison results show that the proposed ANBSVM-based fault diagnosis method makes progress in the performance of fault diagnosis with higher diagnostic accuracy and F1 score.