Are you interested in contributing to address the current challenges faced by the high-tech industry in relation to the development of innovative approaches to smart maintenance in Industry 4.0? Are you fascinated by data-driven approaches, and curious about how the ever increasing amount of data can be exploited to design timely and “just on time” maintenance plans?
PhD candidate in ‘Data-Driven Predictive Maintenance of Complex Engineering Systems Through Robust Optimisation’ (1.0 fte)
within the Operations, Planning, Accounting and Control (OPAC) Group of the Dept. of Industrial Engineering & Innovation Sciences, in collaboration with the Eindhoven Artificial Intelligence Systems Institute (EAISI).
The Eindhoven AI Systems Institute (EAISI) combines all TU/e Artificial Intelligence activities. Top researchers from various research groups work together to create new and exciting AI methodologies and applications with a direct impact on the real world. TU/e has been active in the field of AI for many years, which gives the new institute an excellent starting position to build upon.
Applicants should have completed (or be close to completion of) a Master degree in mathematics, operations management, operations research, econometrics, industrial engineering, or a closely related discipline, with a solid background in mathematical methods. Fluency in English is required.
The high-tech industry is faced every day with the challenge of keeping their systems operational and maximize their availability whilst minimizing maintenance and operational costs. Predictive maintenance enables system downtime and costs to be minimized by acting before failures occur and grouping interventions to share set-up costs and possession time. By developing data-driven decision tools capable to extrapolate knowledge from different data sources and enable more reliable maintenance decisions based on data, with this project we aim at advancing knowledge in data-driven maintenance decision making.
In this project we aim to develop a smart maintenance decision framework for complex multi-component systems, specifically complex machines consisting of many heterogeneous maintainable units (components), which operate in an uncertain environment. Degradation, failure and repair are stochastic processes affected by uncertainty around operating conditions including environmental and usage factors. We envision the framework to be smart and deal with uncertainty by combining learning and updating methods with decision models based on robust optimization to support predictive maintenance planning driven by data from alarms, sensors and process logs. Such a decision framework shall enable robust maintenance policies to be developed so as to mitigate the effects of uncertainty which characterize real-life operation of high-tech systems.
We expect the Ph.D. student to:
More information about the OPAC group can be found at https://www.tue.nl/en/research/research-groups/operations-planning-accounting-and-control/, about the EAISI can be found at https://www.tue.nl/en/research/institutes/eindhoven-artificial-intelligence-systems-institute/.
For this project, you will collaborate with dr. Fecarotti, Dr. Marandi, and prof.dr. Van Houtum.
For more information about this position and the research program, please contact:
dr. C. Fecarotti, e-mail: c.fecarotti[at]tue.nl or dr. A. Marandi, e-mail: a.marandi[at]tue.nl.
For information about terms of employment, click here or contact HRServices.IEIS[at]tue.nl.
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If you are interested, we invite you to apply as soon as possible. You can send us your application through the online job portal of the TU/e: http://jobs.tue.nl/en/vacancies.html. We start interviewing as soon as possible. The due date for applying is December 15, 2021.
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