Opendata, web and dolomites

FAULT-LEARNING SIGNED

Online Class Imbalance Learning for Fault Diagnosis of Critical Infrastructure Systems

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

Project "FAULT-LEARNING" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY OF CYPRUS 

Organization address
address: KALLIPOLEOS STREET 75
city: NICOSIA
postcode: 1678
website: www.ucy.ac.cy

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Cyprus [CY]
 Total cost 157˙941 €
 EC max contribution 157˙941 € (100%)
 Programme 1. H2020-EU.4. (SPREADING EXCELLENCE AND WIDENING PARTICIPATION)
 Code Call H2020-WF-01-2018
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2019
 Duration (year-month-day) from 2019-10-01   to  2021-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY OF CYPRUS CY (NICOSIA) coordinator 157˙941.00

Map

 Project 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.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "FAULT-LEARNING" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "FAULT-LEARNING" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.4.)

P-appetite (2019)

Dissecting how the Drosophila brain regulates behavioral sequences of feeding to ensure protein homeostasis

Read More  

MAGMOLMET (2019)

Magnetic multifunctional molecules based on 4f and 3d/4f metal complexes

Read More  

LC-FMRI (2019)

Deciphering the effects of locus coeruleus activity on whole-brain dynamics and neurovascular coupling

Read More