Real-Time Sensor Fault Detection, Isolation and Accommodation for Industrial Digital Twins

Abstract

The development of Digital Twins (DTs) has bloomed significantly in last years and related use cases are now pervading several application domains. DTs are built upon Internet of Things (IoT) and Industrial IoT platforms and critically rely on the availability of reliable sensor data. To this aim, in this article, we propose a sensor fault detection, isolation and accommodation (SFDIA) architecture based on machine-learning methodologies. Specifically, our architecture exploits the available spatio-temporal correlation in the sensory data in order to detect, isolate and accommodate faulty data via a bank of estimators, a bank of predictors and one classifier, all implemented via multi-layer perceptrons (MLPs). Faulty data are detected and isolated using the classifier, while isolated sensors are accommodated using the estimators. Performance evaluation confirms the effectiveness of the proposed SFDIA architecture to detect, isolate and accommodate faulty data injected into a (real) wireless sensor network (WSN) dataset.