The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

15

Jun

Predictive Maintenance in Modern Radar Systems using Machine Learning

15 June 2026 14:15 to 15:00 |

Master Thesis presentation by Klas Thorgren

Title:  Predictive Maintenance in Modern Radar Systems using Machine Learning
Author: Klas Thorgren
Date & Time: May 15th, 14:15-15:00
Location: Seminar Room M 3170-73 in the M-building, LTH
Supervisor: Bo Bernhardsson and Håkan Warston (Saab)
Examiner:  Björn Olofsson


Abstract:
This thesis investigates how machine learning-based anomaly detection can be applied to enable predictive maintenance in radar systems. The work uses sensors and monitoring data  from a test platform to train and evaluate an LSTM autoencoder for detecting deviations from normal system behavior. It also examines whether the currently available monitoring data, including sensor measurements and fault reports, is sufficient to support a system-level predictive maintenance implementation.

About the event

Location:

Seminar Room M 3170-73 in the M-building, LTH

Contact:

bo [dot] bernhardsson [at] control [dot] lth [dot] se