Abstract: Operation faults in wastewater treatment plant may lead to reduce effluent water quality, raise operation cost and secondary environmental pollution. The representative imbalanced data for fault diagnosis in wastewater treatment process seriously affects the fault diagnosis performance, especially results in the accuracy of faulty classes lower. To address this problem, this paper proposes an improved Bagging ensemble fault diagnosis method based on weighted extreme learning machine in wastewater treatment process. This method establishes the ensemble classifier in Bagging framework, and the weight extreme learning machine algorithm is selected to build the basic classifiers. Defining adjustable over_sampling rate?formula,the diversity of basic classifiers?is?ensured?by?over_sampling?the?minority?data with?SMOTE method.Based on the imbalance classification performance?index?G_mean, a updating formula of the output weight value in the base classifier is defined to improve the recognition accuracy in?faulty?class. Simulation experiments show that the proposed fault diagnosis model over performs the other algorithms. The proposed method can effectively improve G-mean value and overall classification accuracy on fault diagnosis in wastewater treatment process, in particular raise the recognition accuracy in faulty class.

Key words:  imbalanced classification, weighted extreme learning machine, bagging ensemble learning, wastewater treatment, fault diagnosis