Master Thesis: Data-Driven Discovery of Fault Precursors in Gas Turbines

Master Thesis: Data-Driven Discovery of Fault Precursors in Gas Turbines

A Snapshot of Your Day

Modern gas turbines are continuously monitored through many operational and performance measurements. While many faults can be identified once their effects become sufficiently pronounced, subtle changes in operational data may already be present days or weeks before a fault is diagnosed.

This project aims to investigate whether data-driven methods can identify repeatable patterns that appear before known gas turbine faults occur. Historical operational data and documented fault events will be analyzed to understand how measurements evolve before a fault and how early meaningful changes can be detected.

The work will investigate different fault events and explore whether common precursor patterns can be identified across gas turbine units. The results will provide insight into what changes before a gas turbine fault occurs and could contribute to earlier and more informative condition-monitoring strategies.

How You’ll Make An Impact

Analyze historical gas turbine operational data together with documented fault events. Identify and engineer relevant features from operational and performance signals. Develop data-driven and machine-learning methods to identify fault precursor patterns. Investigate how faults evolve and whether precursor patterns are consistent across different units. Evaluate how early developing faults can potentially be detected. Interpret identified patterns from both a data-driven and physical engineering perspective.

What You Bring

Sc. student in Mechanical Engineering, Aerospace Engineering, Engineering Physics, or a similar field. Good programming skills in Python. Experience with machine-learning frameworks such as TensorFlow and/or PyTorch. Familiarity with time-series analysis, data analytics, anomaly detection, or machine learning. Familiarity with good software development practices, including object-oriented programming, modular and reusable code, version control (e.g., Git), testing and documentation. Mechanical engineering knowledge or understanding of physical systems is preferred, particularly within gas turbines, turbomachinery, or thermodynamics. Ability to connect machine-learning results with engineering and physical understanding. Ability to work independently while collaborating with engineers and domain experts. Structured, analytical, and curious, with good communication skills.

About The Team

This master thesis will be carried out in collaboration between the Performance and RDC/ISA departments at Siemens Energy. The collaboration brings together expertise in gas turbine performance and engineering with data analytics and machine learning.

You will work closely with specialists from both departments, providing the opportunity to combine domain knowledge with modern data-driven methods. The teams offer a collaborative environment where you will receive technical guidance while being encouraged to independently explore ideas and develop solutions throughout the thesis.

Our Gas Services division offers Low-emission power generation through service and decarbonization. Zero or low emission power generation and all gas turbines under one roof, steam turbines and generators. Decarbonization opportunities through service offerings, modernization, and digitalization of the fleet.

We can offer you employment benefits such as: reduction of working hours, advance vacation, health care allowance and an eventual possibility to a flexible working place.

Who is Siemens Energy?

At Siemens Energy, we are more than just an energy technology company. With ~100,000 dedicated employees in more than 90 countries, we develop the energy systems of the future, ensuring that the growing energy demand of the global community is met reliably and sustainably. The technologies created in our research departments and factories drive the energy transition and provide the base for one sixth of the world's electricity generation.

Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible. We uphold a 150-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation.

Find out how you can make a difference at Siemens Energy: https://www.siemens-energy.com/employeevideo

Our Commitment to Diversity

Lucky for us, we are not all the same. Through diversity, we generate power. We run on inclusion and our combined creative energy is fueled by over 130 nationalities. Siemens Energy celebrates character – no matter what ethnic background, gender, age, religion, identity, or disability. We energize society, all of society, and we do not discriminate based on our differences.

Application

Don’t hesitate – apply via https://jobs.siemens-energy.com/en_US/jobs , id nr 304271 not later than 2026-10-22

Ongoing selection is applied, the role might be filled before last application date.

We refrain from all contact with staffing and recruitment companies, or advertising brokers.

Location: Finspång

Unionen, [email protected]

Sveriges Ingenjörer & SACO, [email protected]

Ledarna, Anders Fors, [email protected]

IF Metall, Mikael Malmgren, [email protected]