PROGnostics based Reliability Analysis for Maintenance Scheduling

Livrables

The data management plan reports how the project data has been handled during the research project and how it will be after the project completion. The document also provides an update of the project's consortium thoughts on data management and eventual results from third party exploitation of project generated data.

Data Management Plan - preliminary

The preliminary data management plan outlines how the project data is going to be handled both during the research project and after the project completion and includes the project's consortium thoughts on data management.

Project Web portal and Communication activities

This deliverable describes the project web site implementation of features, including the discussion forums and the management methods for project data. Instructions and functionalities available to consortium members and public users are provided here.

Dissemination and Communication Report – final

In this deliverable are listed all the consortium activities toward a proper dissemination of project solutions during the whole project. Firstly the intended audience is described, then a complete list of the publications on national and international journals, conference proceedings and presentations, articles on relevant press, conferences attended is provided. Finally the results of an internal evaluation of the dissemination activities are presented.

Publications

Maintenance schedule optimisation for manufacturing systems

Auteurs: István Németh, Ádám Kocsis, Donát Takács, Basheer W. Shaheen, Márton Takács, Angelo Merlo, Amit Eytan, Luisa Bidoggia, Paolo Olocco
Publié dans: IFAC-PapersOnLine, Numéro 53/3, 2020, Page(s) 319-324, ISSN 2405-8963
Éditeur: Elsevier
DOI: 10.1016/j.ifacol.2020.11.051

Towards accurate robot modelling of flexible robotic manipulators

Auteurs: Z. Arkouli, P. Aivaliotis, S. Makris
Publié dans: Procedia CIRP, Numéro 97, 2021, Page(s) 497-501, ISSN 2212-8271
Éditeur: Elsevier
DOI: 10.1016/j.procir.2020.07.009

Methodology for enabling Digital Twin using advanced physics-based modelling in predictive maintenance

Auteurs: P. Aivaliotis, K. Georgoulias, Z. Arkouli, S. Makris
Publié dans: Procedia CIRP, Numéro 81, 2019, Page(s) 417-422, ISSN 2212-8271
Éditeur: Elsevier
DOI: 10.1016/j.procir.2019.03.072

Identification of dynamic robot’s parameters using physics-based simulation models for improving accuracy

Auteurs: P. Aivaliotis, E. Papalitsa, G. Michalos, S. Makris
Publié dans: Procedia CIRP, Numéro 96, 2021, Page(s) 254-259, ISSN 2212-8271
Éditeur: Elsevier
DOI: 10.1016/j.procir.2021.01.083

The use of Digital Twin for predictive maintenance in manufacturing

Auteurs: P. Aivaliotis, K. Georgoulias, G. Chryssolouris
Publié dans: International Journal of Computer Integrated Manufacturing, Numéro 32/11, 2019, Page(s) 1067-1080, ISSN 0951-192X
Éditeur: Taylor & Francis
DOI: 10.1080/0951192x.2019.1686173

Machines’ Behaviour Prediction Tool (BPT) for maintenance applications

Auteurs: P. Aivaliotis, E. Xanthakis, A. Sardelis
Publié dans: IFAC-PapersOnLine, Numéro 53/3, 2020, Page(s) 325-329, ISSN 2405-8963
Éditeur: Elsevier
DOI: 10.1016/j.ifacol.2020.11.052

Maintenance Service Platform (MSP) for maintenance information collection and sharing

Auteurs: A. Eytan, A. Nichita
Publié dans: IFAC-PapersOnLine, Numéro 53/3, 2020, Page(s) 330-335, ISSN 2405-8963
Éditeur: Elsevier
DOI: 10.1016/j.ifacol.2020.11.053

PROGRAMS project approach to maintenance management

Auteurs: M. Surico, R. Ricatto, A. Merlo, I. Németh, A. Sardelis, M. Villoslada, E. Montejo, N. Frenkel, P. Aivaliotis, I.M. de la Pera Celada, J. Sidiropoulos, A. Eytan, A. Papavasileiou, F. Aggogeri
Publié dans: IFAC-PapersOnLine, Numéro 53/3, 2020, Page(s) 313-318, ISSN 2405-8963
Éditeur: Elsevier
DOI: 10.1016/j.ifacol.2020.11.050

Robotic System Reliability Analysis and RUL Estimation Using an Iterative Approach

Auteurs: Francesco Aggogeri, Riccardo Adamini, Panagiotis Aivaliotis, Alberto Borboni, Amit Eytan, Angelo Merlo, István Németh, Claudio Taesi, Nicola Pellegrini
Publié dans: Advances in Service and Industrial Robotics - Proceedings of the 28th International Conference on Robotics in Alpe-Adria-Danube Region (RAAD 2019), Numéro 980, 2020, Page(s) 134-143, ISBN 978-3-030-19647-9
Éditeur: Springer International Publishing
DOI: 10.1007/978-3-030-19648-6_16

A RUL calculation approach based on physical-based simulation models for predictive maintenance

Auteurs: P. Aivaliotis, K. Georgoulias, G. Chryssolouris
Publié dans: 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC), 2017, Page(s) 1243-1246, ISBN 978-1-5386-0774-9
Éditeur: IEEE
DOI: 10.1109/ice.2017.8280022

Predictive maintenance framework: Implementation of local and cloud processing for multi-stage prediction of CNC machines’ health

Auteurs: P. Aivaliotis, K. Georgoulias, R. Ricatto, M. Surico
Publié dans: 2018
Éditeur: I-ESA

Prognostics based Robust Design to strengthen mechanical system functionalities

Auteurs: Francesco Aggogeri, Angelo Merlo, István Németh, Nicola Pellegrini, Alberto Borboni, Amit Eytan, Claudio Taesi
Publié dans: 2018
Éditeur: EURO 2018

Design for Reliability of Robotic Systems Based on the Prognostic Approach

Auteurs: Francesco Aggogeri, Nicola Pellegrini, Claudio Taesi, Monica Tiboni
Publié dans: 2019 23rd International Conference on Mechatronics Technology (ICMT), 2019, Page(s) 1-5, ISBN 978-1-7281-3998-2
Éditeur: IEEE
DOI: 10.1109/icmect.2019.8932106

Using digital twin for maintenance applications in manufacturing: State of the Art and Gap analysis

Auteurs: Panagiotis Aivaliotis, Konstantinos Georgoulias, Kosmas Alexopoulos
Publié dans: 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), 2019, Page(s) 1-5, ISBN 978-1-7281-3401-7
Éditeur: IEEE
DOI: 10.1109/ice.2019.8792613

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