PhD Stipend in Machine Learning for Monitoring Wind Turbine Operation

at  Aalborg Universitet

At the Technical Faculty of IT and Design, Department of Electronic Systems, a PhD stipend in Machine Learning for Monitoring Wind Turbine Operation is available within the general study programme Electrical and Electronic Engineering. The stipend is open for appointment from 15 August 2024, or soon as possible thereafter.

Department of Electronic Systems employs more than 200 people, of which about 90 are PhD students, and about 40 % of all employees are internationals. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU’s problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active role in transferring inventions and results into applications in close collaboration with industrial partners worldwide. You can read more about the department at www.es.aau.dk.

JOBBESKRIVELSE

The project is carried out in a collaboration between Aalborg University and Siemens-Gamesa, and we are seeking a PhD candidate for a project focused on the development and application of machine learning techniques for monitoring the health of the main bearing in wind turbines. This innovative research aims to enhance the operation of next-generation offshore wind turbines through the development of a novel condition monitoring system.

Responsibilities:

• Develop machine learning algorithms and methods for condition monitoring of a disruptive new main bearing technology.
• Analyze sensor data from both normal operations and destructive tests conducted on small-scale and full-scale test rigs.
• Create interpretable predictors for known failure modes, identify characteristic fault features, and extract informative features from multimodal data.
• Apply advanced techniques such as Kernel Density Estimation and Topological Data Analysis for fault detection and localization.

The project will be conducted under the auspices of the Learning and Decisions research group. This group specializes in developing control and decision-making strategies for autonomous systems and infrastructures, integrating physical models with pervasive data. It combines three key areas of research: optimization(encompassing multi-objective optimization, dynamic programming, and reinforcement learning), safety and resilience evaluation, and the secure, privacy-preserving implementation of control algorithms.
You may obtain further information from Prof. Rafal Wisniewski, Department of Electronic Systems, email:raf@es.aau.dk, concerning the scientific aspects of the stipend.
PhD stipends are allocated to individuals who hold a Master’s degree. PhD stipends are normally for a period of 3 years. It is a prerequisite for allocation of the stipend that the candidate will be enrolled as a PhD student at the Technical Doctoral School of IT and Design in accordance with the regulations of Ministerial Order No. 1039 of August 27, 2013 on the PhD Programme at the Universities and Certain Higher Artistic Educational Institutions. According to the Ministerial Order, the progress of the PhD student shall be assessed at regular points in time.
Shortlisting will be applied. This means that subsequent to the deadline for applications the head of department supported by the chair of the assessment committee will select candidates for assessment. All applicants will be informed whether they will be assessed or not.
For further information about stipends and salary as well as practical issues concerning the application procedure contact Ms. Lisbeth Diinhoff, The Doctoral School at The Technical Faculty of IT and Design, email:ld@adm.aau.dk, phone:+45 9940 9589.
For more information of The Technical Doctoral School of IT and Design:www.phd.tech.aau.dk
The application is only to be submitted online by using the"Apply online” button below.
AAU wishes to reflect the diversity of society and welcomes applications from all qualified candidates regardless of personal background or belief.

• Develop machine learning algorithms and methods for condition monitoring of a disruptive new main bearing technology.
• Analyze sensor data from both normal operations and destructive tests conducted on small-scale and full-scale test rigs.
• Create interpretable predictors for known failure modes, identify characteristic fault features, and extract informative features from multimodal data.
• Apply advanced techniques such as Kernel Density Estimation and Topological Data Analysis for fault detection and localization