Research Engineer - MOIRA - Automatic Multi-sensor Validation Methods

Job Description

Marie Curie Early Stage Researcher – MOIRA – Automatic multi-sensor validation methods


Siemens Digital Industries Software (SISW), headquartered in Leuven, Belgium, will have an open PhD position in 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 an 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 research methods that enable the automatic detection of “incorrect” sensor data. Sensors are exposed to tough operating conditions in many industrial environments (e.g., excavation machines driving on off-road tracks, gantry cranes in steel mills, etc.). Therefore, a common problem is the occurrence of “measurement anomalies”, i.e., where part of the data is incorrect in the sense that there are some deviations from what was intended to be measured. Examples of measurement anomalies with particular shapes are dropouts, offsets, drifts and spikes, but the measurement anomaly can also be a more subtle problem with the data. An advanced automatic sensor validation method is thus highly sought after.

The ESR will investigate machine-learning methods that are trained to recognize incorrect sensor data. A systematic approach will be followed: in the first stage, a supervised learning approach will be adopted, whereby it is assumed that an historical dataset with fully labelled examples is available. As this assumption might not prove to be practically realizable in many cases, an unsupervised anomaly-detection approach will be investigated in the second stage. Such an approach does not require labelled data, but is typically more difficult to implement successfully compared to a supervised approach. An interesting third alternative that will be investigated is a semi-supervised approach, where a small labelled dataset (e.g., obtained from expert user feedback) is available in addition to the larger unlabelled dataset. Besides the systematic investigation outlined above (supervised – unsupervised – semi-supervised), a particular focus point will be to leverage the fact that there are multiple sensors, i.e., there is a certain redundancy in the measurement setup so that some sensors will be measuring related quantities. While measurement anomalies are non-physical events that occur at random times (so that they will likely not be observed in multiple sensor channels), real physical events likely affect multiple (closely located) sensors. A comparison between sensor pairs (e.g., linear or nonlinear correlation analysis) could thus be exploited in order to better detect the measurement anomalies (for example, to distinguish an incorrect measurement spike from a true physical shock event in the data).

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

Candidate Profiles:

Applicants must have a MSc degree or equivalent in mechanical/mechatronic engineering 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.

·       Previous hands-on experience with large channel count measurement campaigns, such as in automotive, heavy industries or aerospace testing facilities will be an advantage.

Marie Curie eligibility Criteria

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: Experienced Professional

Job Type: Full-time

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