Inferential Sensors in an Extended Kalman Filter for Fault Estimation

Fault estimation is crucial for ensuring reliability and safety throughout industrial processes. However, the increased nonlinearity and complexity in modern systems, as well as their feedback control logic, multiply the challenges when estimating faults. Health monitoring in today's systems may impact the overall cost substantially. To address such challenges, we present a hybrid fault estimation scheme for nonlinear systems, by incorporating an Extended Kalman Filter along with inferential sensors. These fault-sensitive sensors are developed using symbolic regression combined with information theory, to be cost-effective supplements to the existing hard sensors. The proposed method was applied to open-loop and closed-loop architectures of a plate-fin cross-flow heat exchanger dynamic model toward estimating the fault severity at various levels of measurement noise. To showcase the robustness of the inferential sensors, we compared the performance of the proposed framework to an Extended Kalman Filter designed solely with information from hard sensor measurements.