Master Thesis: Machine Learning for Anomaly Detection in Heavy Equipment

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Master’s Thesis: Machine Learning for Anomaly Detection in Heavy Equipment

Background

Volvo Construction Equipment (VCE) designs and manufactures machines for a wide range of industries, including construction, mining, and forestry. Equipment such as wheel loaders, articulated haulers, and excavators is used in demanding operating environments, where breakdowns can be costly and must therefore be minimized.

VCE is investigating methods to improve the reliability and robustemess before the machines reach customers. In this thesis, you will contribute by developing machine learning algorithms to identify anomalous behaviour during system-level testing.

Thesis Description

The VCE product verification process includes extensive system-level testing. The transmission is one example of a critical component that undergoes compliance-related testing. During these tests, time-series data is collected from multiple sensors, including measurements of temperature, pressure, speed, and other relevant parameters.

This thesis will investigate machine learning algorithms for analysing data generated at one of VCE’s testing facilities, with the aim of identifying anomalous machine behaviour that may indicate faults, incorrect system performance, or other issues. In addition, the thesis will propose an approach for implementing the selected algorithm as a complement to the existing testing methodology.

The task may include:

• Reviewing relevant methods for time-series analysis and anomaly detection.

• Preparing and analysing data.

• Developing and evaluating suitable machine learning methods.

• Proposing an approach for integrating the algorithm into the testing process.

Expected Background

Applicants should meet the following requirements:

• Be enrolled in a master’s programme at a university in Sweden.

• Have an interest in mechanical engineering and machine learning.

• Have experience with TensorFlow or a similar machine learning framework.

Applicants should be able to demonstrate technical competence in machine learning or a related field. As this project also focuses on mechatronic systems, an interest in this area would be beneficial. Please describe your relevant experience in your cover letter, including applicable coursework, projects, or previous work experience.

The thesis will involve collaboration with engineers at VCE in Eskilstuna. Students are therefore expected to work on-site in Eskilstuna during the thesis project.

The thesis is for 30 ECTS per student.

• If you are a single applicant, you might need to collaborate with an additional thesis student.

• If you are a pair of students that want to work together, explain in your cover letter. Send in two separate applications.

Ready for the next move?

Application deadline: 2026-11-30

The selection process will involve an online interview. The students will be contacted after review of the application.

Expected start date: Last week of January 2027 or First Week of February 2027

Expected duration: End of May 2027 or first week of June 2027

Contact person and email address: pierre.thoren@volvo.com and varun.gopinath@volvo.com

We value your data privacy and therefore do not accept applications via mail.

Who we are and what we believe in We are committed to shaping the future landscape of efficient, safe, and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents across the group’s leading brands and entities.

Applying to this job offers you the opportunity to join Volvo Group. Every day, you will be working with some of the sharpest and most creative brains in our field to be able to leave our society in better shape for the next generation. ​We are passionate about what we do, and we thrive on teamwork. ​We are almost 100,000 people united around the world by a culture of care, inclusiveness, and empowerment.

Part of Volvo Group, Volvo Construction Equipment is a global company driven by our purpose to build the world we want to live in. Together we develop and deliver solutions for a cleaner, smarter, and more connected world. By unleashing everyone’s full potential, we build a more sustainable future for all our stakeholders. Come join our team and help us build a better tomorrow.