Machine Learning on Field Test Data

Thesis background


Volvo Penta has a vested interest in utilizing and understanding vast amounts of data. As Martin Lundstedt (Volvo Group President and CEO) recounts, “It’s important to have good data management and ownership [of data], because then on top, you can create a lot of smart solutions.”
To that end, we at Volvo Penta Field Test (VPFT) have applied data engineering principles to build a large-scale data lake. This has enabled us to create a data-driven and cloud-native workflow for advanced analytics using Microsoft Azure and Databricks. Along with our data-driven workflow, data engineering and analytics have enabled us to reach valuable strategic conclusions and optimizations.
During this endeavor, technologies such as Artificial Neural Networks (ANN), evolutionary algorithms, semantic segmentation, and various clustering techniques have become core components of our current workspace. The most recent thesis project at VPFT created a deep learning process for detection and semantic segmentation of engine drive cycles.
We at VPFT are now committed to taking the next step in our machine learning journey.


Thesis Scope of Work


Our purpose with this thesis is to study real world engine usage data to further our understanding of engine behavior and applications. Thanks to the wealth of data collected by VPFT, we see multiple different possible projects. We will here present the outline for a possible project, but welcome applicants to come with their own ideas and suggestions.
Our suggested approach is to cluster engines based on logged data. A project is currently ongoing at Penta to cluster engines based on aggregated data from production engines. This thesis would complement that project by also investigating the possibilities of clustering on data from Field Tests, where we have more data for each engine. This thesis could then focus in on identifying better signals to use for clustering, detecting engines that are outliers in their behavior, or predicting errors using fault code data.
If this type of project sounds interesting, we would like to have a meeting with you where we can further discuss your thesis. As stated, we are also open to other avenues of investigation that you may be thinking of.


Workspace Subset


A selection of tools and technologies we currently use in day-to-day operations.
Python 3
Pandas
NumPy
SciPy
Azure
DevOps
Data Factory
Functions
Cosmos DB
SQL Server
Synapse
SQL
Databricks
Apache Spark (PySpark)
Power BI

Qualifications & Required Documents


You should possess these qualifications:
Master student in any of the following fields
Artificial Intelligence
Computer Science
Data Science
Machine Learning
Physics
Knowledge of machine learning algorithms and big data
Some programming proficiency

Your application should include the following:
CV
Cover Letter
Transcript of grades

Contact


Andreas Nyman, Manager Field Test and Data Management, andreas.nyman@volvo.com
Kindly note that due to GDPR, we will not accept applications via mail. Please use our career site.


About Field Test


Volvo Penta Field Test is divided into two primary areas, field test activities and Volvo Penta Data Management.
Field test activities are divided into Marine- and Industrial product segments. The field test engineers are responsible for test objects connected to selected OEMs (Original Equipment Manufacturers) or customers and for a specific engine range depending on the projects. The responsibility covers planning and start-up of field tests objects as well as support during the test execution, and completion of test.
Volvo Penta Data Management are responsible for structuring data and building data science tools. We are principally working on data infrastructure, setting up data storage structures from raw data, building advanced analytics tools, and producing a data-driven culture and collaborative environment.
You will be part of a global and diverse team with highly skilled and passionate professionals who trust each other, and embraces change to stay ahead. We make our customers win.


Practical Information


Thesis Level: Master (30 ECTS points)
Language: English
Starting date: January 2023
Number of students: 2 students
Last application date: 1st of December 2022


Examiner proposal:
Entity: AB Volvo Penta
State/Province: Västra Götaland
City/Town: Göteborg
Employment/Assignment Type: Thesis
Functional Area: Technology