Recurrent neural network based sensor fault detection and isolation for nonlinear systems: Application in PWR

Abstract

Real-time monitoring of sensors is crucial in attaining consistent process performance with strict safety and eco-friendly measures. This article presents a fault detection and isolation (FDI) technique that diagnoses sensor faults regardless of the utilized model structure. A Recurrent Neural Network (RNN) is utilized to estimate state variables with the inputs and outputs of the process. RNNs build predictive models of the process. Then, a bank of residuals of state variables is obtained such that each residual is responsive to a subsection of faults and unresponsive to others. Consequently, an exclusive fault signature is attained for each fault case. One of the merits of the proposed procedure is that it does not necessitate the fault history of the process or first principle models contrasting to other existing outcomes in the literature. The efficacy of the proposed FDI methodology is studied on a highly nonlinear pressurized water nuclear reactor model.