Ensemble WELM method for imbalanced learning in fault diagnosis of wastewater treatment process

Highly imbalanced data for fault diagnosis in wastewater treatment process seriously affects fault diagnosis performance, especially in identification of faulty classes. Reduced recognition accuracies of faulty classes may lead to occurrence of other issues, such as failure to reach quality standard of effluent water, high operation cost and secondary pollution. An ensemble weighted extreme learning machine method (WELM) for imbalanced learning was proposed for fault diagnosis modeling in wastewater treatment process. AdaBoost ensemble classification algorithm based on WELM base classifiers was integrated into assessment index G-mean of imbalanced classification. New updating rules for initial weight matrix in the base classifiers and ensemble weight formula were defined for iterative learning of the base classifiers. Simulation results show that this fault diagnosis model of wastewater treatment process can improve classification performance, such as G-mean value, overall classification precision, and recognition accuracy of faulty classes. The proposed method is effective in imbalanced fault diagnosis of wastewater treatment process. © All Right Reserved.