Modular design

Architecture

Pure mathematics

Computer network

Mathematics

Operating system

Soft sensor

Data mining

Statistics

Machine learning

Engineering

Visual arts

Actuator

Intelligent sensor

Embedded system

Estimator

Fault detection and isolation

Artificial intelligence

Wireless sensor network

Computer science

Real-time computing

Field (mathematics)

Process (computing)

Artificial neural network

Classifier (UML)

IEEE Sensors Journal

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.

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