Marie Curie Early Stage Researcher – MOIRA – Transfer learning for end-of-line testing and monitoring in fleets
Siemens Digital Industries Software, in Leuven, Belgium will have an open PhD position in the frame of the European Training Network on Monitoring Large-Scale Complex Systems (“MOIRA”), funded by the European Commission through the H2020 “Marie Skłodowska-Curie Innovative Training Networks” (ITN) program.
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. This objective will be achieved by having 15 early-stage researchers (ESRs) in a collaborative network between top European universities, research institutes, wind-turbine, and plant operators, OEMs and industrial stakeholders with expertise in mechanical engineering, computer science, signal processing, vibrations, inverse problems, operations maintenance, data analytics, and networks.
The PhD project connected to this vacancy involves SISW as lead beneficiary and the KU Leuven (KUL) as the academic partner and PhD awarding entity. The ESR will become part of the research team of the SISW TEST division and will cooperate closely with the SISW staff as well as other international visiting researchers and students. The ESR will be enrolled as a PhD student in the KUL doctoral school.
PhD Project Description:
The ESR will focus on novel methodologies for assessing the performance and usage of assets in a fleet throughout the product lifecycle. A specific application of interest is the end-of-line quality control testing of vehicles, based on NVH (Noise, Vibration, Harshness) measurements. Machine learning and deep learning techniques have the potential to automatically and objectively assess the status of the vehicle based on such measurements. However, these techniques rely on the availability of a sufficiently large training dataset, which may be infeasible to obtain in industrial practice (the so-called data scarcity problem). To overcome the data scarcity problem, the ESR will research two transfer learning strategies.
In the first strategy, a physical simulation model will provide a source dataset that can be used for training of an initial machine learning model. Such an approach was already successful in previous work on bearing fault detection, where simulation models were used for training a support vector machine or a deep neural network. However, it is expected that other cases will require more advanced transfer learning methods (in particular, domain adaptation), for example, if the simulations cannot capture all features which will be present in real-life data and which are relevant for the specific industrial context.
The second strategy assumes that the machine-under-study is part of a larger fleet of similar (but not necessarily identical) machines. For example, if older machines have been in the field already for some years, the data and knowledge gained on these machines might be transferrable to predict quantities on a newly deployed machine.
Timeline and remuneration: The earliest start time is 1st March 2021. The Marie Curie grant foresees funding for a duration of 3 years, however, given that a typical PhD duration in Belgium is 4 years, extra funds are reserved such that a fourth PhD year can be added. Furthermore, in order to stimulate intersectoral and international mobility, the ESR will have short research visits (so-called “secondments”) to at least two Beneficiary/Partner Organisations (with the secondments not exceeding 30% of the duration of the doctoral training).
The remuneration is generous and will be in line with the EC rules for Marie Curie grant holders. It consists of a salary augmented by a mobility allowance, resulting in a net monthly salary of about 1900-2300 Euro depending on family status.
Supervisors and main contacts:
Siemens Digital Industries Software: dr. Bram Cornelis (research manager)
KUL: prof. Konstantinos Gryllias, prof. Wim Desmet
Applicants must have a MSc degree or equivalent in mechanical/mechatronic or related field, which will qualify for starting a PhD programme.
They must have:
· Excellent qualification in engineering disciplines such as mechanics, electronics, physics and mathematics;
· Very strong interest in machine learning
· Experience with scientific computing and high-level programming languages such as Matlab or Python.
· Affinity with the scientific research methodology;
· Interest to develop and implement a long-term research programme leading to a PhD;
· Capability to work independently and in a team;
· Fluent in spoken and written English;
Competences that are considered as an additional advantage:
· Previous hands-on experience with machine learning (incl. deep learning) is a huge asset.
· Solid background in experimental vibration and/or acoustic testing and signal processing is an advantage;
· Solid background in CAE methodologies and software is an advantage;
Marie Curie eligibility
To be eligible, you need to be an "early stage researcher" i.e. simultaneously fulfill the following criteria at the time of recruitment:
· Mobility: you must not have resided or carried out your main activity (work, studies, etc...) in Belgium for more than 12 months in the 3 years immediately prior to your recruitment under the MOIRA project.
· Qualifications and research experience: you must be in the first 4 years of your research career after the master degree was awarded.
Organization: Digital Industries
Company: Siemens Industry Software NV
Experience Level: Early Professional
Job Type: Full-time