Interval Observer Based Fault Diagnosis for Wastewater Treatment Biological Processes

This paper proposes an interval observer based fault diagnosis method for the biological growth process in wastewater treatment. First, the nonlinear microbial growth model is transformed into a Takagi-Sugeno (T-S) fuzzy model with unknown but bounded disturbances and measurement noise. Then, considering
$H_{\infty}$
disturbance attenuation conditions, a T-S interval observer is designed for the obtained T-S fuzzy system to obtain interval boundaries of the states. Furthermore, an interval residual can be calculated by the interval states. Next, the sensor fault signals can be detected by examing that whether zero belongs to the residual interval. Finally, simulation results are presented to validate the feasibility of the proposed fault diagnosis method in wastewater treatment biological processes.