PhD position Predictive Maintenance in Steel Manufacturing (SUPREME)

26 dagen geleden

Arbeidsvoorwaarden

Standplaats:
Drienerlolaan, Enschede, Overijssel
Dienstverband:
Tijdelijk contract / Tijdelijke opdracht
Uren per week:
38 - 38 uur
Salarisindicatie:
€ 2191 - € 2801 per maand
Opleidingsniveau:
WO

Maintenance is vital in ensuring the availability, reliability and cost effectiveness of technical systems like Tata Steel’s production facilities. However, the degradation of systems is a dynamic process, governed by changes in both the ...

Functieomschrijving

Maintenance is vital in ensuring the availability, reliability and cost effectiveness of technical systems like Tata Steel’s production facilities. However, the degradation of systems is a dynamic process, governed by changes in both the system and its environment. The challenge is to achieve just-in-time maintenance. So: “How can advanced sensing technology and modelling of degradation and failure processes be used to develop a predictive maintenance concept for production systems ?”

The approach followed to answer this consists of four important steps.

  1. The appropriate sensors must be selected to collect the relevant data (what, how and where to measure). Contrary to many other approaches we do not just deploy a large amount of sensors (big data approach), but aim to understand the critical failures of the system, assess the most relevant parameters and select the required sensor type, location and quality.
  2. Collect the required data in an efficient and flexible manner. While wireless communication in industrial environments is extremely challenging due to the high variability of the radio channel in terms of error rates, channel downtimes and error bursts caused by various equipment, one of the key challenges in developing a wireless sensor network is to introduce reliable and robust networking protocols that have real-time capability.
  3. Development of models, based on the physics of failure, to predict the critical failures in the system due to e.g. wear, fatigue, corrosion or creep. The collected data on usage, loads and environmental conditions will be used as input for the models. As opposed to the common approach of data analytics, which heavily rely on training with historic data, a physical model-based approach has the advantage that not only previously encountered failures could be predicted.
  4. Combine the data collection with the model development and demonstrate the integral concept on a real production facility. Tata Steel has offered the Hisarna steel production pilot plant as case study for this purpose.

This project is sponsored by NWO-TTW, and will be executed in close collaboration with Tata steel, IJssel Technologie and Semiotic Labs. We are now looking for a PhD candidate working on the following topic:

Development of physical degradation and failure models for the critical components in the production facility, aiming to accurately predict upcoming failures based on the collected data on operating and environmental conditions. Typically models for fatigue, creep and overload as a result of changing thermos-mechanical loads will have to be developed and implemented.

In the same project, another PhD student will work on the development of reliable and robust networking protocols that have real-time capability.

Functie-eisen

A sound theoretical background in Mechanical Engineering, Applied Physics, Materials Science or a similar topic at academic level (MSc degree), and preferably affinity with maintenance. We welcome ambitious candidates with strong communication skills who like to present their work at conferences and project meetings. Fluency in English is required. You need to provide IELTS test results (minimum score 6.5), TOEFL-iBT (minimum score 90) or Cambridge CAE or CPE. An interview and a scientific presentation will be part of the selection procedure.

Information and application
For more information on the research at the Pervasive Systems group (PhD A) or Dynamics based Maintenance group (PhD B), see ps.ewi.utwente.nl/ or www.utwente.nl/ctw/tm/research/DBM. Additional information about these vacancies may be obtained from prof. dr. Paul Havinga () or prof. dr. ir. Tiedo Tinga, t.tinga@utwente.nl.

Your application should comprise a letter of motivation, a curriculum vitae, a list with grades of courses attended, contact information of 2 referees and, if applicable, a list of publications. Applications should be sent before May 31th , 2017, through the link below.

Arbeidsvoorwaarden

We offer a 4-year full-time position as a PhD candidate appointed at the University of Twente (UT).

In accordance with the Collective Labour Agreement for Dutch Universities, a PhD salary starts at € 2.191,- gross per month in the first year and extends to a maximum of € 2.801,- gross per month in the fourth year. The University of Twente provides excellent facilities for professional and personal development, a holiday allowance (8%) and an end-of-year bonus (8.3%) and a number of additional benefits.

Bedrijfsomschrijving

The University of Twente. We stand for life sciences and technology. High tech and human touch. Education and research that matter. New technology which drives change, innovation and progress in society. The University of Twente is the only campus university in the Netherlands; divided over five faculties we provide more than fifty educational programmes. The University of Twente has a strong focus on personal development and talented researchers are given scope for carrying out pioneering research.

The Faculty of Engineering Technology (ET) is one of the five faculties of the University of Twente. ET combines Mechanical Engineering, Civil Engineering and Industrial Design Engineering. Our faculty has approximately 1800 bachelor and master students, 400 employees and 150 PhD candidates. The departments of the faculty cooperatively conduct the educational programmes and participate in interdisciplinary research projects, programmes and the research institutes: MIRA, CTIT, SBE and IGS.

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