Summary
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.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/955681 |
| Start date: | 01-03-2021 |
| End date: | 28-02-2025 |
| Total budget - Public funding: | 3 958 164,36 Euro - 3 958 164,00 Euro |
Cordis data
Original description
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.Status
SIGNEDCall topic
MSCA-ITN-2020Update Date
28-04-2024
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Organisations
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| KATHOLIEKE UNIVERSITEIT LEUVEN (Coördinator)
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| AMC VIBRO SPOLKA Z OGRANICZONA ODPOWIEDZIALNOSCIA
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| Cranfield University
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| FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.
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| INSTITUT NATIONAL DES SCIENCES APPLIQUEES DE LYON
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| Ikerlan
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| LULEA TEKNISKA UNIVERSITET
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| SAFRAN SA
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| SIEMENS INDUSTRY SOFTWARE NV (SISW)
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| THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
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| University of Modena and Reggio Emilia
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| Wroclaw University of Science and Technlogy - Politechnika Wroclawska