The Nonlinear Dynamics Group in the Department of Mechanical and Process Engineering and the Chair of Structural Mechanics & Monitoring in the Department of Civil, Environmental and Geometric Engineering of ETH Zurich are seeking one doctoral student for a project on structural health monitoring and fault diagnostics for train components. The position is funded by a recently awarded SCCER Mobility grant.
Continual use and exposure to rough environments weaken critical components of railway assets exposing them to possible failures. In particular, the project focuses on the wheelset axle, where damage may initiate as a small crack, which can slowly grow due to fatigue and lead to failure. To timely detect damage, predict deterioration and assure safety, current railway vehicles are subjected to regular checks, which imply significant downtime, decrease in availability and an increase in operational costs.
The project aims at developing diagnostic techniques to be applied in operation, for assessing the state of the wheelset axle. The acquired data, constituted by accelerations and strains, will be paired with a reduced order model, i.e., with a computationally fast approximation of a high-fidelity numerical model, to predict the response of the component in presence of a crack of arbitrary shape and position. Each PhD project will tackle one of these applications. However, significant cross-feeding of methodologies and collaboration between the supervisors’ chairs is expected. The close-to-real-time predictive capabilities of the reduced order model allows for fusion with sensor data - in operation - and leads to early detection of possible faults.
The project will be embedded the Mobility SCCER-funded SENTINEL: In-SErvice diagnostics of the cateNary/panTograph and wheelset axle systems through INtELligent algorithms. SENTINEL aims at developing diagnostic techniques to be applied in operation, for assessing the condition of critical train components (with the pantograph/catenary system considered in a further PhD project).
You will drive the research in the field of structural health monitoring for the wheelset axle. In particular, the focus will be on the following tasks:
The position reles on close collaboration with involved industrial project partners (including Siemens and SBB) from different departments. The collaboration will be focused on for data collection, data pre-processing, model sharing, development of the solutions and their knowledge transfer to the application field.
Moreover, the position involves:
We are looking for a PhD student with a strong analytical background and an outstanding Msc degree in Engineering, Physics, Applied Mathematics, or a related field. You should have a proven experience in structural dynamics, finite element method and model order reduction. Good programming skills are required, as well as experience with commercial Finite Element programs. A general knowledge of sensor technology for vibration detection and structural health monitoring is desiderable. Professional command of English (both written and spoken) is mandatory. German is an advantage. We expect the candidate to be self-driven with strong problem solving abilities and out-of-the-box thinking. The duration of the PhD position is foreseen for three years.
We look forward to receiving your online application with the following documents:
Only complete applications containing all the required documents will be considered. Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered. The review of applications will continue until the position is filled, with the position to start as soon as possible.
Questions regarding the position (no applications) should be directed to
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