Robust Decentralized Context-Aware Sensor Fault Detection with In-Place Self-Calibration

There is a high demand in advanced fault detection methods suitable for sensor networks monitoring complex dynamic systems such as industrial plants or large infrastructure units. This paper proposes a robust and efficient decentralized sensor fault detection method with in-place sensor self-recalibration capability that extracts and uses complex context information referred to the full monitored process. The method includes three main components, all decentralized and sharing the same statistical framework: 1) a consensus-based modeling step based on decentralized RANSAC; 2) a statistical analysis based on Bayesian networks and Hidden Markov Models in which each sensor identifies inconsistencies with the consensus model and determines if it is correctly calibrated, uncalibrated or faulty and; 3) a final step in which each uncalibrated sensor self-recalibrates using the consensus model. The proposed method is efficient in the use of computational and communicational resources, it is scalable and robust against outliers, transmission errors, sensor failures and network topology changes. It has been extensively validated in an experimental industrial setting.