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Sensor Validation for Digital Twins of Safety-Critical Systems


The project shall develop a systematic framework for sensor-fault detection, isolation, and accommodation by forcing a paradigm shift towards the development and the integration of signal processing and machine learning methodologies into novel hybrid-analytics solutions. Building upon ground-breaking concepts from graph signal processing, deep learning and transfer learning, SIGNIFY shall design and test tailored strategies from a Bayesian perspective to be used as tools for sensor validation when importing data from physical assets into digital systems. Designing optimization strategies exploiting real-time real-world data from sensors is one main value from the digital transformation. Unfortunately, sensors are prone to failures and injection of corrupted data into digital twins generates erroneous planning. When operating in closed loop, erroneous planning may lead to consequences ranging from performance degradation to lack of safety and risk of danger. The need for a validation tool before injecting sensor data into the digital twin is urgent in safety-critical applications. Among relevant areas, Industry 4.0 focuses on development of safety-critical systems, where the high level of accuracy is needed when validating sensor data. In these systems it is hard to predict a malfunction by looking at the data without prior knowledge of the underlying phenomenon. Results from SIGNIFY will be general enough to apply to a large variety of scientific/application domains, however during the project 2 uses cases within the Industry 4.0 framework will be considered: (UC1) Flow Assurance for CO2 Transport Operations; (UC2) Low-Temperature CO2 Liquefaction and Phase Separation for Carbon Capture. The facilities selected will allow the integration of physical models to benchmark sensor data and fit the Bayesian approach employed in SIGNIFY to combine signal processing and machine learning techniques. Performance improvement will be assessed in terms of validation accuracy.

Project leader: Pierluigi Salvo Rossi

Started: 2021

Ends: 2024

Category: Universiteter

Sector: UoH-sektor

Budget: 15914000


Address: Trondheim