基于核函数的加权极限学习机污水处理在线故障诊断

Online fault diagnosis in wastewater treatment process by kernel-based weighted extreme learning machine

污水生化处理中的运行故障会引起出水水质不达标、运行费用增高和环境二次污染等严重问题,需要及时准确地对运行故障进行诊断。考虑到污水处理过程运行状态数据的不平衡性造成故障诊断准确率下降,提出了一种基于核函数的加权极限学习机污水处理过程实时在线故障诊断方法。该方法以极限学习机为基础,采用加权的方式处理数据的不平衡特性,通过核函数的非线性映射来提高数据线性可分的程度。仿真实验证明,本文建立的污水处理在线故障诊断模型在线测试精度高,泛化性能好,模型在线更新速度快,能够比较好地满足准确性和实时性,实现对污水处理过程的在线故障诊断。

Operation faults in biochemical wastewater treatment process often result in serious issues such as effluent water below quality specification, high operation cost, and secondary environmental pollution, therefore spontaneous and accurate diagnoses are required. Considered the poor accuracy of fault diagnosis induced by imbalanced characteristics of the process data in wastewater treatment, a novel online fault diagnostic model for wastewater treatment process was proposed, i.e., the kernel-based weighted extreme learning machine. Based on extreme learning machine（ELM） theory, weighting scheme was used to resolve the data imbalance and the non-linear mapping of kernel function was used to improve the extent of linear separation. Simulation experiments showed that this online fault diagnostic model has higher measuring precision, better generalization ability, and faster online updating speed, and meet the requirement of accuracy and spontaneity. Therefore, the proposed method can be applied in real-time on-line fault diagnosis in wastewater treatment process.