污水处理过程故障会导致出水水质下降、运行费用增高甚至造成环境的二次污染，而污水处理故障诊断数据的典型不平衡特性，严重影响了故障诊断的效果，尤其会导致故障类别的正确率偏低。针对此问题，提出一种基于加权极限学习机的改进型Bagging集成污水处理故障诊断建模方法。以加权极限学习机为基分类器，以Bagging集成框架建立集成分类器。定义可调整的过采样倍率公式，通过SMOTE方法对少数类样本进行过采样，保证基分类器间的多样性。以不平衡分类性能指标G-mean值为基础，定义新的基分类器输出权值更新公式，提高故障类别识别率。仿真实验表明，该污水处理故障诊断模型的性能优于其他对比算法，可有效提高G-mean值和整体分类正确率，特别是提高了故障类别的识别正确率。

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.