A Data-Driven Architecture for Sensor Validation Based on Neural Networks

In this paper, we propose a novel sensor validation architecture, which performs sensor fault detection, isolation and accommodation (SFDIA). More specifically, a machine-learning based architecture is presented to detect faults in sensors measurements within the system, identify the faulty ones and replace them with estimated values. In our proposed architecture, sensor estimators based on neural networks are constructed for each sensor node in order to accommodate faulty measurements along with a classifier to determine the failure detection and isolation. Finally, numerical results are presented to confirm the effectiveness of the proposed architecture on a publicly-available air quality (AQ) chemical multi-sensor data-set.