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.