This paper explores an innovative method for distributed state estimation aimed at reducing computational complexity while detecting sensor faults in natural gas pipelines. The proposed framework utilizes a partial-distributed ensemble Kalman filter (EnKF), comprising linear local filters and a nonlinear main filter. The main filter handles non-linear computations during the time update, while the simultaneous operation of linear local filters manages linear computations during the measurement update. These local filters generate distinct local state estimates based on their specific sensor measurements, which are then transmitted to an information mixer to compute fault-free state estimates. Moreover, a fault diagnosis strategy is developed using local state variances and residuals. Faulty sensors are identified and isolated by comparing these metrics against a threshold. Additionally, an adaptive thresholding approach is incorporated to enhance effective fault identification. The effectiveness of the proposed technique is demonstrated in systems characterized by high nonlinearity and dimensionality, and featuring simultaneous multiple sensor faults, through extensive simulations and comparative analyses.

A new multiple access scheme based on energy discrimination is proposed. Such a scheme is based on the differences in the average power received by each user. It is particularly useful when no extra resources are available by means of existing schemes; moreover, it is useful when the system complexity must be kept very limited.