Sensor-Fault Detection, Isolation and Accommodation for Digital Twins via Modular Data-Driven Architecture

Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time raw data into digital twins. However, sensors might be unreliable due to inherent issues and/or environmental conditions. This article aims at detecting anomalies in measurements from sensors, identifying the faulty ones and accommodating them with appropriate estimated data, thus paving the way to reliable digital twins. More specifically, we propose a general machine-learning-based architecture for sensor validation built upon a series of neural-network estimators and a classifier. Estimators correspond to virtual sensors of all unreliable sensors (to reconstruct normal behaviour 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 data-sets is conducted and the performance of the proposed architecture validated under hard and soft synthetically-generated faults.

Automated summary

This article introduces a machine-learning-based sensor validation architecture that uses neural network estimators and a classifier to detect and isolate faulty sensors for reliable digital twins. Results show that the proposed architecture performs well under different real-world datasets and synthetically-generated faults.