Model-based Sensor-Fault Detection and Isolation in Natural-Gas Pipelines for Transient Flow

Sensor-based monitoring of natural gas pipelines is crucial for safety and detecting sensor faults is pivotal for reliable operations. This paper investigates the problem of fault diagnosis in a natural gas pipeline under transient flow, characterized by a system of hyperbolic partial differential equations (PDEs). A data-fusion approach based on an unscented Kalman filter (UKF) is employed to perform sensor-fault detection and isolation (SFDI). The architecture consists of a bank of local UKFs to provide the state estimates, which are further analyzed to identify sensor faults. The performance achieved by the proposed method is promising even in the case of a nonlinear flow model.