The project SIGNIFY focuses on the verification and validation of data collected from sensors. Digital twins are a revolutionary product from the current digital transformation with a broad range of applications, including safety-critical areas. Digital twins enable effective strategies for monitoring and planning of various activities and they rely on real-time real-world sensor measurements. Unfortunately, data from sensors may be corrupted and their injection into the digital twin might lead into erroneous action planning. The main objective of SIGNIFY is to develop methodologies (and assess their performance) for preventing corrupted data to be processed by the digital twin (with focus on safety-critical applications) and avoid erroneous action planning whose consequences range from performance degradation to lack of security and risk of danger.
Flexible architectures for detecting, isolating and accommodating sensor faults have been designed and validated on different real-world datasets. Performance of proposed solutions are excellent and superior to the state of the art. Scalability with the number of sensors is a critical issue to be mitigated.
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.