Wastewater treatment sensor fault detection using RBF neural network with set membership estimation

Abstract

There are many sensors used to monitor the quality of the effluent during the wastewater treatment process. So the normal monitoring of the sensor is critical to wastewater treatment. In this article, the proposed sensor fault diagnosis method is based on fault diagnosis of interval prediction which using RBF neural network with set membership estimation. After some input and output data of the WWTP are obtain, an interval containing the actual output of the system without a fault can be easily predicted. If the sensor measured is out of the predicted interval, it can be determined that a fault has occurred. This paper also establishes two independent interval diagnosis models to further make sure whether the senor is faulty or the system is faulty. The results demonstrate that the proposed sensor fault diagnosis method is effective and useful.