The proposed research track runs at the KU Leuven Mecha(tro)nic System Dynamics (LMSD) division which currently counts more than 100 researchers and is part of the Department of Mechanical Engineering, a vibrant environment of more than 300 researchers (www.mech.kuleuven.be). Doctoral training is provided in the framework of the Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd). LMSD has a longstanding history and internationally highly recognized expertise in the fields of condition monitoring, numerical modeling, engineering dynamics, automotive engineering, vibro-acoustic analysis, identification and robust optimal control of (non-) linear systems, active control and lightweight structure design and analysis. It is also recognized for its yearly Modal Analysis (ISMA) and Acoustics (ISAAC) courses and for organizing the biennial ISMA Noise and Vibration Engineering Conference (www.isma-isaac.be). The research group has a long track record of combining excellent fundamental academic research with industrially relevant applications, leading to dissemination in both highly ranked academic journals as well as on industrial fora. Furthermore, the group contributes to the Flanders Make@KU Leuven Motion Products University Core Lab of Flanders Make. Flanders Make (https://www.flandersmake.be/en) is the strategic research centre for the manufacturing industry in Flanders, stimulating open innovation through excellent research. The research group's international research flavour is illustrated amongst others by the large portfolio of research projects (https://www.mech.kuleuven.be/en/mod/Projects) which includes regional, national and international funded activities through which the group cooperates with leading mechatronic and machine & vehicle-building companies in Flanders and throughout Europe. More information on the research group can be found on the website: https://www.mech.kuleuven.be/en/research/mod/about and our Linked.In page: https://www.linkedin.com/showcase/noise-&-vibration-research-group/. The PhD will be supervised by Prof. Konstantinos Gryllias.
Website unit
Project
This PhD is part of the Horizon Europe MSCA Doctoral Network PATRON. European manufacturing is at the centre of a twin ecological and digital transition, being both driver and subject to these changes. At the same time, manufacturing companies must maintain technological leadership and stay competitive. The size and the complexity of the associated challenges - such as the integration of Artificial Intelligence, the use of industrial data, the transformation into a circular economy and the need for agility and responsiveness - requires pooling of resources and a novel approach of cooperation. The objective of the PATRON project is to develop the next generation of PHM methodologies, algorithms and technologies, so enabling condition monitoring, with the focus on real-time diagnostics and prognostics. This objective will be achieved by having 10 Doctoral Candidates (DCs) working closely and interacting frequently in this inter-disciplinary and multi-disciplinary area. Despite remarkable progresses in health monitoring boosted by new technologies and AI, most approaches still rely on the use of rudimentary HIs defined more than half a century ago. On the other hand the Community of Tribology is working at the micro and the macroscale of the contacts where loads are applied and wear, damage and faults occur. Impressively enough the two communities, Condition Monitoring/Prognostics and Health Management and Tribology, are following separate paths. The proposed PATRON project brings together the two communities and doctoral candidates 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 PATRON 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 next generation Prognostics and Health Management techniques.
DC2 will work on the development of a physics inspired machine learning technique trying to combine the advantages of purely data driven approaches with the advantages of model based approaches. Advanced drivetrain models, consisted of submodels of bearings, gears and shafts, will be developed including faults such as pitting and spalling. Multibody and FEM models will be combined in order to achieve the needed accuracy. The developed models will be merged with data driven approaches in different directions. As usually there are not enough data in order to train a machine learning approach at the faulty classes, the validated model will be used in order to generate artificial data which will cover the measurement space and can be used for the training of a machine learning methodology. Additionally the embedding of the physical model in an artificial neural network will be considered, either by preparing part of the architecture of the network based on the physical model, or by putting the physical model and the data model sequentially, so the data model can practically model the missing physics of the physical model. The combination of the two models will lead to a digital twin which can be further used for diagnostics and prognostics. The models and the methodologies will be tested, evaluated and validated using dedicated test rigs of drivetrains of different power.
Innovative aspects: Physical inspired machine learning approach for condition monitoring, Hybrid models.
Profile
If you recognize yourself in the story below, then you have the profile that fits the project and the research group.
- I have a master degree in engineering, physics, computer science or mathematics and performed above average in comparison to my peers.
· I haven’t had residence or main activities in Belgium for more than 12 months in the last 3 years.
- I am proficient in written and spoken English.
- I have a genuine interest in combining sensing techniques, signal processing, machine learning, first principle models and measurement approaches into an innovative toolchain for condition monitoring of drivetrains and I have experience with (at least) some of these topics.
- I have interest in measurements and set up development
- I have good programming skills in Matlab and/or in Python.
- As a PhD researcher of the KU Leuven Mecha(tro)nic System Dynamics (LMSD) division I perform research in a structured and scientifically sound manner. I read technical papers, understand the nuances between different theories and implement and improve methodologies myself.
- Based on interactions and discussions with my supervisors and the colleagues in my team, I set up and update a plan of approach for the upcoming 1 to 3 months to work towards my research goals. I work with a sufficient degree of independence to follow my plan and achieve the goals. I indicate timely when deviations of the plan are required, if goals cannot be met or if I want to discuss intermediate results or issues.
- In frequent reporting, varying between weekly to monthly, I show the results that I have obtained and I give a well-founded interpretation of those results. I iterate on my work and my approach based on the feedback of my supervisors which steer the direction of my research.
- I value being part of a large research group which is well connected to the machine and transportation industry and I am eager to learn how academic research can be linked to industrial innovation roadmaps.
- During my PhD I want to grow towards following up the project that I am involved in and representing the research group on project meetings or conferences. I see these events as an occasion to disseminate my work to an audience of international experts and research colleagues, and to learn about the larger context of my research and the research project.
Offer
- A remuneration package competitive with industry standards in Belgium, a country with a high quality of life and excellent health care system.
- An opportunity to pursue a PhD in Mechanical Engineering, typically a 4 year trajectory, in a stimulating and ambitious research environment.
- Ample occasions to develop yourself in a scientific and/or an industrial direction. Besides opportunities offered by the research group, further doctoral training for PhD candidates is provided in the framework of the KU Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd), known for its strong focus on both future scientists and scientifically trained professionals who will valorise their doctoral expertise and competences in a non-academic context. More information on the training opportunities can be found on the following link: https://set.kuleuven.be/phd/dopl/whytraining.
- A stay in a vibrant environment in the hearth of Europe. The university is located in Leuven, a town of approximately 100000 inhabitants, located close to Brussels (25km), and 20 minutes by train from Brussels International Airport. This strategic positioning and the strong presence of the university, international research centers, and industry, lead to a safe town with high quality of life, welcome to non-Dutch speaking people and with ample opportunities for social and sport activities. The mixture of cultures and research fields are some of the ingredients making the university of Leuven the most innovative university in Europe (KU Leuven is the Most Innovative University of Europe – Faculty of Arts). Further information can be found on the website of the university: https://www.kuleuven.be/english/living
Interested?
To apply for this position, please follow the application tool and enclose:
1. Full CV – mandatory
2. Motivation letter – mandatory
3. Full list of credits and grades of both BSc and MSc degrees (as well as their transcription to English if possible) – mandatory (when you haven’t finished your degree yet, just provide us with the partial list of already available credits and grades)
4. Proof of English proficiency (TOEFL, IELTS, …) - if available
5. Two reference letters - if available
6. An English version of MSc thesis, or of a recent publication or assignment - if available
For more information please contact Prof. dr. ir. Konstantinos Gryllias, tel.: +32 16 32 30 00, mail: [email protected].
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