Master’s degree projects
Here we present topics available for Master’s degree projects (“exjobb” in Swedish) within Sentio at Lund University. Students are also invited to contact project supervisors directly.
A degree project corresponds to approximately 20 weeks of full-time study (30 credits) and marks the end of the study programme and the start of professional life. All degree projects are carried out in collaboration with a university department, but they can take different forms. Some are conducted in partnership with companies, often providing opportunities for employment. Others are more research-focused, conducted entirely within the academic environment, and can serve as an entry point for a career in academia.
Degree projects offered by Sentio partners should address topics relevant to the competence centre and the broader Swedish manufacturing industry. Each project is carried out independently by the student, who develops in-depth knowledge about a specific issue within the chosen area. To explore and analyse the issue, the student will apply knowledge from various courses and gain new insights through collaboration with the partner.
Current project offers
At the moment we have no open offers but if you are interested in doing a degree project in Sentio you are always welcome to reach out to Anna-Karin Alm (anna-karin [dot] alm [at] ftf [dot] lth [dot] se (anna-karin[dot]alm[at]ftf[dot]lth[dot]se)).
Ongoing and completed projects
Acoustic leak detection signal analysis
Project with Alfa Laval
Supervisors: Axel Knutsson, Alfa Laval & Maria Sandsten, Mathematical Statistics.
Author: Oscar Stackenland
Description: Alfa Laval produces millions of heat exchangers every year and among those some are subject to faults and leakage. To find and classify these faults, a huge amount of time has to be expended by technicians and materials experts. The goal with this master thesis project is to explore if this process can be done more efficiently by looking at sound recordings of water-filled heat exchangers which give rise to air-bubbles with clear popping sounds once it reaches the water surface.
Title and link to thesis: Acoustic Leak Classification in Heat Exchangers by Time-Frequency Analysis and Machine Learning
Classification of brake discs
Project with LTH
Supervisors: Maria Sandsten, Mathematical Statistics, and Oleksandr Gutnichenko, Production and Materials Engineering.
Author: Emil Svalfors
Description: Accurate classification of time-varying and non-stationary time-series signals is a central problem in many scientific and engineering disciplines, including bioacoustics, seismology and climate science. The aim of this study is to investigate the possibility of connecting of time-frequency representations (TFRs) with machine learning techniques to improve signal classification. The idea behind the study was to investigate if analysis of sound can be used to differentiate between brake discs in good, versus bad, working order.
Title and link to thesis: Optimizing Time-Frequency Representations for Time-Series Signal Classification Using Neural Networks