Master Thesis: Anomaly detection in time series for heavy-duty battery electric vehicles

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:
The Advance Analytics for Uptime team within Volvo GTT is focused on delivering quality products based on fact-based decisions and a data-driven approach. The team aims to improve the quality of the products, reduce development costs, and enhance customer experience through its connected services.

Ensuring operational safety is paramount to commercial heavy-duty vehicles. The objective of this project is to develop anomaly detection methods for multi-variate time series data, collected using on-board sensors from electric vehicles. 

We believe you:
•    Are a student studying an MSc program in computer science, machine learning or equivalent.
•    Have strong knowledge in anomaly detection and representation learning via deep learning-based methods; knowledge in causal inference and reservoir computing is a merit.
•    Practical experience working with multi-variate time-series dataset is a merit.
•    Feel confident in programming in Python and/or PySpark, preferably with some experience in relevant libraries and frameworks for anomaly detection and deep learning, such as PyOD, PyTorch, Keras, or Tensorflow.

Thesis Description:
As a master thesis student, you will be working data collected from Volvo trucks of Volvo trucks for developing and testing anomaly detection algorithm. You will work closely with the Advanced Analytics Team and have the opportunity to collaborate with other domain experts in different tech streams. In addition, you will be part of a highly motivated team working with data-driven methods for predictive maintenance, increasing the uptime of heavy-duty vehicles.

In this project, we will develop and explore the use of representation learning methods, e.g. deep learning-based approaches (including time series embedding methods), that can capture and encode key characteristics of time series data for anomaly detection. As an example, many state-of-the-art time series anomaly detection methods utilize autoencoders, where learned embeddings in the latent features or reconstruction errors were utilized to compute the anomaly score. In addition, methods that are inherently explainable (e.g. causal relations learned via causal inferences), can be learned in an incremental setting (e.g. decision tree based approaches) or computationally efficient (e.g. echo state network via reservoir computing), are of great interest as well. The developed approach will be evaluated and compared with a few time series embedding methods on a real-world dataset collected from EVs.

Objectives and learning outcome:
•    Literature survey including review of related machine learning methods, e.g. anomaly detection, and representation learning, time-series embedding methods etc.
•    Develop and implement representation learning and anomaly detection methods.
•    Evaluate the proposed approach and compare it with state-of-the-art methods on a EVs dataset.
•    Write a Master Thesis report and present the results at the company.
•    Upon successful completion of this work, a feasibility study for further research on this domain is desirable.

Duration:
•    The duration of the Thesis is 20 weeks (Approx.).
•    The thesis starts in January 2025 or earlier if possible.
•    30 ECTS (academic credits) if in agreement with your Thesis Advisor in the University.
•    This thesis is suitable for 1-2 students; 
•    On-site presence is preferred and appreciated, as it is easier to collaborate with our teams, as well as getting access to various resources. 

Last application date is: 26th of November. If you have questions pease contact carlos.camacho@volvo.com

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

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Group Trucks Technology are seeking talents to help design sustainable transportation solutions for the future. As part of our team, you’ll help us by engineering exciting next-gen technologies and contribute to projects that determine new, sustainable solutions. Bring your love of developing systems, working collaboratively, and your advanced skills to a place where you can make an impact. Join our design shift that leaves society in good shape for the next generation.