A Machine-Learning Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins

Abstract

Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time raw data into digital twins (DTs). However, sensors might be unreliable due to inherent issues and/or environmental conditions. This article aims at detecting anomalies instantaneously in measurements from sensors, identifying the faulty ones and accommodating them with appropriate estimated data, thus paving the way to reliable DTs. More specifically, a real-time general machine-learning-based architecture for sensor validation is proposed, built upon a series of neural-network estimators and a classifier. Estimators correspond to virtual sensors of all unreliable sensors (to reconstruct normal behavior and replace the isolated faulty sensor within the system), whereas the classifier is used for detection and isolation tasks. A comprehensive statistical analysis on three different real-world datasets is conducted and the performance of the proposed architecture is validated under hard and soft synthetically generated faults.