The proposed research track runs at the CNH Industrial in Italy and will be supervised by the KU Leuven Mecha(tro)nic System Dynamics (LMSD). CNH Industrial is a global leader in capital goods that implements design, manufacturing, distribution, commercial and financial activities in international markets. We employ more than 64,000 people in 66 manufacturing plants and 57 research and development centers in 180 countries. Our global presence and broad reach mean that we can capitalize on opportunities for growth and pursue our ambition to become a leader in our sectors. Through our 12 brands we make the vehicles that keep agriculture and industry growing. From tractors and combines to trucks and buses, as well as powertrain solutions for on-road and off-road and marine vehicles, we design, produce, and sell machines for work.
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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.
DC8 will work on innovative techniques for the optimal management of agricultural vehicles in an open field based on the determination of the machine condition while in operation and by considering sudden changes of weather conditions. Following agriculture 4.0, nowadays agricultural vehicles are very sophisticated and equipped with many sensors and actuators. Making the optimal decision is usually very difficult because information is not easily obtained and integrated. Information about the technical condition or health status of the machine, the cost of maintenance or loss of production, and customer information, are not defined in the same units and are not provided on a consistent time scale. Some data is constantly updated e.g., health status data, but data like customer information is usually extracted from a historical data that is fixed over time. DC8 will develop an intelligent maintenance system, based on intelligent data processing systems, in order to exploit heterogeneous data. DC8 will develop methodologies for selection of minimum set of sensors, optimal data collection and optimal data fusion for multiple sensor detection systems.
Innovative aspects: Multi-time scale/multisensor AI based maintenance strategy for health monitoring under varying operating and environmental conditions
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 Italy 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 gearboxes 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 and CNH 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 CNH Italy, 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.
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] and Nicola Raule (CNH) [email protected]
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