Thesis supervisor: István Németh
Location of studies (in Hungarian): BME Gyártástudomány és -technológia Tanszék Abbreviation of location of studies: GTT
Description of the research topic:
a.) Preliminaries:
The analysis of the reliability and maintenance of machine tools and manufacturing systems was dealt with in previous international research projects at the Department of Manufacturing Science and Engineering. Currently, we have a well-functioning methodology and software tools to determine optimal maintenance strategies and optimal preventive maintenance cycles. The aim of the research is to further develop this methodology by developing new maintenance strategy models (e.g., opportunistic), degradation models, fault prediction methods and optimisation methods.
b.) Aim of research:
Development of new methods and software tools to support the planning and scheduling of optimal maintenance of manufacturing equipment (machine tools, robots or production systems).
c.) Tasks, main items, necessary time:
• Literature search: research and analysis of statistical methods, maintenance data collection and analysis methods, maintenance strategies, degradation models, maintenance planning and scheduling methods, failure forecasting methods and life-cycle cost models, with special attention to historical load levels and the involvement of artificial intelligence methods. (0.5 years)
• Development of methods for determining performance indicators related to degradation, historical load levels, reliability and maintenance (0.5 years).
• Development of evaluation procedures (objective functions, e.g. life-cycle cost, availability, remaining useful life) (0.75 years).
• Development of artificial intelligence methods. (0.5 years)
• Research, analysis and development of optimisation methods (0.5 years).
• Implementation of methods in a computer program (0.75 years).
• Writing the dissertation (0.5 years).
d.) Required equipment:
The necessary software tools are available at the Department of Manufacturing Science and Engineering.
e.) Expected scientific results:
New methods and software tools for maintenance planning and scheduling of manufacturing equipment.
f.) References:
1. Basheer Shaheen, Ádám Kocsis, István Németh: Data-driven failure prediction and RUL estimation of mechanical components using accumulative artificial neural networks, Engineering Applications of Artificial Intelligence, Volume 119, March 2023, 105749, https://doi.org/10.1016/j.engappai.2022.105749
2. Shaheen, Basheer Wasef; Németh, István: Integration of Maintenance Management System Functions with Industry 4.0 Technologies and Features—A Review, Processes 2022, 10, 2173 , 27 p. (2022), https://doi.org/10.3390/pr10112173
3. Basheer Shaheen, István Németh: Machine Learning Approach for Degradation Path Prediction Using Different Models and Architectures of Artificial Neural Networks. Periodica Polytechnica Mechanical Engineering, 66(3), pp. 244–252, 2022, https://doi.org/10.3311/PPme.20145
4. István Németh, Ádám Kocsis, Donát Takács, Basheer W. Shaheen, Márton Takács, Angelo Merlo, Amit Eytan, Luisa Bidoggia, and Paolo Olocco: Maintenance schedule optimisation for manufacturing systems, Proceedings of the 4th IFAC Workshop on Advanced Maintenance Engineering, Service and Technology, AMEST 2020, September 10-11, 2020. Cambridge, UK, pp. 319-324.
5. 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: PROGRAMS project approach to maintenance management, Proceedings of the 4th IFAC Workshop on Advanced Maintenance Engineering, Service and Technology, AMEST 2020, September 10-11, 2020. Cambridge, UK, pp. 313-318.
6. Alice Reina, Ádám Kocsis, Angelo Merlo, István Németh, and Francesco Aggogeri: Maintenance decision support for manufacturing systems based on the minimization of the life cycle cost, Procedia CIRP, Volume 57, 2016, pp. 674-679.
7. EASE-R3: Integrated framework for a cost-effective and ease of repair, renovation and re-use of machine tools within modern factory. Európai uniós kutatási projekt, EU FP7 NMP, NMP2-LA-2013-608771, időtartam: 2013. július 1. – 2016. június 30.
8. PROGRAMS: Prognostics based Reliability Analysis for Maintenance Scheduling, Európai uniós kutatási project, EU H2020-FOF, szerződés száma: 767287, időtartam: 2017. október 1. – 2021. március 31.
Number of students who can be accepted: 1
Deadline for application: 2024-10-15
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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