Master Thesis: Machine learning algorithms for predicting failures to enable proactive maintenance

Job description

Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future? If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match. 

 

Background:

 

Predicting potential failures in machinery can enable proactive maintenance work. Proactive refers to the ability to plan and coordinate maintenance work well in-advance. There is a real need to ensure that machines don't fail during  operations as any machine down-time is a significant revenue loss for users of construction equipment.

 

 

Description:

 

The thesis explores the development and implementation of data-oriented algorithms to predict failures in complex systems within construction equipment, focusing on scenarios where computational resources and data availability are limited. With the increasing reliance on construction machinery for critical operations, predictive maintenance has become essential to minimize downtime, reduce costs, and enhance operational efficiency. However, traditional methods often require extensive computational power and vast datasets, posing challenges in resource-constrained environments.

 

The goal of the thesis is to design lightweight, efficient algorithms that leverage limited data to identify potential failure patterns in machinery. The study will focus on techniques such as feature selection, data augmentation, and Physics Informed machine learning models optimized for small-scale datasets. The student will work with systems within drivelines (Engine/motorm Transmission, Axles and Brakes). The expectation is to test the development in current machines to verify and validate the result. There is an urgency for failure detection methods and therefore the ambition is to industrialize and deploy the solutions in a short time-frame.

 

 

Who:

A master student undergoing a 5 year civilingenör program or 2 years master program in Sweden. The student should be interested in product development. We expect a cover letter which includes one short paragraph on why you are interested in this work and one paragraph on ideas for achieving the thesis goals.

 

When:

Start and compete before July 2025 (flexible)

 

Contact: Varun Gopinath

 

Last application date: 18th of December, 2024.

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

 

Who we are and what we believe in 
Our focus on Inclusion, Diversity, and Equity allows each of us the opportunity to bring our full authentic self to work and thrive by providing a safe and supportive environment, free of harassment and discrimination. We are committed to removing the barriers to entry, which is why we ask that even if you feel you may not meet every qualification on the job description, please apply and let us decide.

 

Applying to this job offers you the opportunity to join Volvo Group. Every day, across the globe, our trucks, buses, engines, construction equipment, financial services, and solutions make modern life possible. We are almost 100,000 people empowered to shape the future landscape of efficient, safe and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents with sharp minds and passion across the group’s leading brands and entities. 

 

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. 

Job Category:  Technology Engineering

Organization:  Volvo Construction Equipment

Travel Required:  No Travel Required

Requisition ID:  15835