Projet MOIRA (Horizon 2020, appel à projets 2020)
Action : Doctoral Networks
Panel : Information Science and Engineering
Statut du projet : SIGNED

Modern technological systems increase in scale and are becoming more and more complex and sophisticated. Parallel, the revolution in electronics, digital technology and communications have drastically modified and expanded the physical diversity, scope, processing capabilities and complexity of the monitoring equipment and infrastructure used. Millions of networked sensors are being embedded in the physical world sensing, creating and communicating data. The amount of data available for capturing has been exploding and the era of Big Data is already here, as the Internet of Things (IoT) is becoming a reality. The main question which arises is how, following which steps and with which tools the data can be transformed to information and knowledge. The objectives of MOIRA are i) the development of novel signal processing tools for the monitoring of industrial processes based on machine learning methods applied on heterogeneous time series, ii) the application of data mining technologies for the estimation of Key Performance Indicators which determine the operational profit, iii) the conception, development and validation of methodologies for automated monitoring of cyber physical system fleets, iv) the multi sensor machine condition monitoring under variable operating conditions. The proposed MOIRA project (MOnItoRing of large scale complex technologicAl systems) brings together early stage researchers and experienced specialists from key players in academia and industry across Europe covering different scientific disciplines and industrial stakeholders from a broad range of backgrounds to optimally tackle the challenges ahead. The MOIRA Fellows will be trained in innovative PhD topics as well as receiving specific theoretical and practical education in the fields of mechanical engineering and computer science, focusing towards the online early accurate identification of abnormal incidents with minimum false alarms and missed detections.

Mots clés : condition monitoring, process monitoring, fleet monitoring, sensor fusion, fault detection, diagnosis, prognosis, vibrations

Participants :
- Siemens (Belgium) (Belgique) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 512640.0 €
- Safran (France) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 274802.04 €
- Ikerlan (Espagne) — Partner, beneficiary, Recherche, subvention : 250904.88 €
- Fraunhofer Society (Allemagne) — Partner, beneficiary, Recherche, subvention : 252788.4 €
- University of Modena and Reggio Emilia (Italie) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 261499.68 €
- KU Leuven (Belgique) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 512640.0 €
- Wrocław University of Science and Technology (Pologne) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 227478.6 €
- Luleå University of Technology (Suède) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 281982.96 €
- Amc vibro spolka z ograniczona odpowiedzialnoscia (Pologne) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 227478.6 €
- Institut national des sciences appliquées de Lyon (France) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 549604.08 €
- University of Oxford (Royaume-Uni) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 303172.56 €
- Cranfield University (Royaume-Uni) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 303172.56 €