Abstract
The monitoring of natural gas pipelines is highly dependent on the information provided by different types of sensors. However, sensors are prone to faults, which results in performance degradation and serious hazards such as leaks or explosions. To prevent catastrophic failures and ensure the safe and efficient operation of the pipelines, it is crucial to timely diagnose sensor faults in natural gas pipelines. This paper investigates model-based sensor fault diagnosis techniques in a natural-gas pipeline under transient flow. A fusing architecture based on distributed data fusion is used for implementing the sensor fault detection, isolation, and accommodation (SFDIA) mechanism. The fusing architecture consists of a set of local filters and an information mixer. The local filters estimate the state variables in parallel, which are subsequently transferred to the information mixer to evaluate the sensor faults and compute fault-free state estimates. In this paper, three different types of fusing filters, namely based on the ensemble Kalman filter (EnKF), fusing unscented Kalman filter (UKF), and fusing extended Kalman filter (EKF) are investigated for fault diagnosis. Results demonstrate that all three filters can successfully detect, isolate, and accommodate sensor faults.