Project description
A new way to detect faults in critical infrastructure
Critical infrastructures are the foundation on which communities are built. They provide indispensable goods and services. Failure – faults and malfunction – in a critical infrastructure can lead to massive economic losses. This is why prompt detection is vital. The EU-funded FAULT-LEARNING project will design and develop an online learning-based fault diagnosis engine to monitor and analyse data arriving in real time from critical infrastructure systems. This will involve online learning capabilities and address class imbalance, which is a major challenge for learning systems as faults are not high-probability events. The project will also deliver a prototype for sensor fault diagnosis that will be evaluated on a physical ‘Smart Buildings’ testbed to enable its efficient testing under realistic conditions.
Objective
The aim of the project is to design and develop an online learning-based fault diagnosis engine with adaptation capabilities. This engine will monitor and analyse data arriving in real time from critical infrastructure (CI) systems, to accurately detect a potential fault and effectively isolate and identify its exact location. Modern society relies heavily on the availability and smooth operation of CI systems, such as electrical power systems, water distribution systems and telecommunication networks. In such large-scale, complex engineering systems when a failure occurs due to faults, it can have severe societal, health and economic consequences. The sequential arrival of data in CI systems calls for a fault diagnosis engine with adaptive behaviour to achieve and maintain optimal performance. However, the vast majority of existing work falls short on this requirement. This project will incorporate online learning capabilities to achieve adaptability and will also address class imbalance, a major challenge for learning systems, arising from the fact that faults are low probability events. Online class imbalance learning (OCIL) is an emerging research topic focusing on the combined challenges of online learning and class imbalance. We will shed light on supervised OCIL as very few methods currently deal with this problem and address for the first time the unsupervised and semi-supervised OCIL problems. The proposed algorithms will be evaluated in realistic fault diagnosis datasets from industrial partners and in an advanced Smart Buildings simulator allowing us to run sensor fault scenarios in large-scale multi-zone buildings. Furthermore, a prototype on sensor fault diagnosis will be delivered that will be evaluated on a physical Smart Buildings testbed to enable its efficient testing under realistic conditions. Overall, this novel and interdisciplinary project will provide invaluable insights on incorporating learning capabilities in CI systems fault diagnosis.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.4. - SPREADING EXCELLENCE AND WIDENING PARTICIPATION
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF-EF-ST - Standard EF
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-WF-2018-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
1678 Nicosia
Cyprus
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.