{"id":1701,"date":"2021-01-21T14:33:59","date_gmt":"2021-01-21T13:33:59","guid":{"rendered":"http:\/\/dmc.pwr.edu.pl\/?page_id=1701"},"modified":"2022-02-28T15:07:04","modified_gmt":"2022-02-28T14:07:04","slug":"the-european-training-network-on-monitoring-large-scale-complex-systems-moira","status":"publish","type":"page","link":"https:\/\/dmc.pwr.edu.pl\/index.php\/the-european-training-network-on-monitoring-large-scale-complex-systems-moira\/","title":{"rendered":"The European Training Network on Monitoring Large-Scale Complex Systems (MOIRA)"},"content":{"rendered":"\n

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 shape proffesionals able to face challenges of the era of Big Data .<\/p>\n\n\n\n

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Leuven (from https:\/\/h2020-moira.eu)<\/figcaption><\/figure><\/div>\n\n\n\n

The objective of the MOIRA project is to develop the next generation of knowledge discovery methodologies, algorithms and technologies, so enabling data-driven, plant-wide fleet monitoring, with the focus on real-time diagnostics and prognostics.<\/p>\n\n\n\n

The main tasks in which WUST is involved: <\/strong><\/p>\n\n\n\n

ESR9\n will focus on the analysis of long-term, heterogeneous, historical \nbig-data from time-varying systems operating under impulsive, \nnon-Gaussian noise. Mining machines are very specific: unique, complex \nand designed for heavy duty, they operate under non-stationary \nconditions, they can experience multiple faults and they are frequently \noverloaded (especially during start-up or emergency stops). The \ncondition monitoring of these machines is complicated. Often, \ndiagnostics measurements are affected by the environmental conditions \n(temperature) and impulsive disturbances (impulsive noise). The \nmonitoring of such machines requires the processing of a huge number of \ndifferent variables and the nearly online validation and processing of \ndata. The ESR will develop methodologies in order to extract and exploit\n the knowledge about the evolution of the monitoring process, exploiting\n information from different types of sensors and signals.<\/p>\n\n\n\n

Innovative aspects<\/em>: Development of novel \nmodelling\/analysis\/separation approaches for long-term, big Data, \nheterogeneous processes for condition monitoring with a focus on \nnon-stationary operation and heavy-tailed, non-Gaussian noise.<\/em><\/p>\n\n\n\n

ESR14 will develop methodologies to fuse and exploit information extracted \nfrom heterogeneous data, including vibration, temperature, images, sound\n and local environmental parameters, captured by inspection robots, for \nmonitoring and diagnosing machines and infrastructure (electric cables, \npipelines, structures) used in deep mines. Due to the harsh environment \nand natural hazards (gas and rock outbursts, seismic risks), the future \nof mining is related to introducing fully automated machinery and \nrobotic inspection systems in order to minimize the risks for humans. \nThe development of mining-inspection robots equipped with various types \nof sensors, including microphones, accelerometers, \ntemperature\/humidity\/gas sensors and cameras, will provide a massive and\n heterogeneous amount of data gathered under harsh mining conditions.<\/p>\n\n\n\n

Innovative aspects\n : Development of data-fusion techniques for the exploitation of \nheterogeneous data captured by robotic devices for monitoring of mines.<\/em>
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Duration<\/td>1 March 2021 – 28 February 2025<\/td><\/tr>
Partners<\/td> – Leuven.Inc vzw
– Tetra Pak Packaging Solutions S.p.A.
– Technische Universit\u00e4t Darmstadt
– University of the Basque Country <\/td><\/tr>
Beneficiaries<\/td>– Katholieke Universiteit Leuven
– Institut National des Sciences Appliqu\u00e9es de Lyon
– University of Oxford
– Cranfield University
– Lule\u00e5 University of Technology
– University of Modena and Reggio Emilia
– Wroclaw University of Science and Technology
– Fraunhofer-Gesellschaft
– IKERLAN
– SIEMENS Industry Software NV
– SAFRAN SA
– AMC VIBRO <\/td><\/tr>
Budget<\/td> \u20ac 3 958 164,36 <\/td><\/tr>
Funding<\/td> European Union\u2019s Horizon 2020
Sk\u0142odowska-Curie grant agreement n\u00b0 955681<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Find out more – https:\/\/h2020-moira.eu\/<\/a>.<\/p>\n\n\n\n

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This project has received funding from the European Union\u2019s Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie grant agreement n\u00b0955681.<\/p>\n<\/div><\/div>\n\n\n\n

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Project partners:<\/p>\n\n\n\n

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