Detekce poruch sekundárního tlumení vlakového podvozku pomocí strojového učení

Abstract

This bachelor thesis focuses on detecting faults in the secondary suspension of railway bogies using machine learning methods. The aim is to analyse vibration data and process it in a way that allows reliable distinction between a normal, fault-free state and suspension faults of different severity levels. The work deals with data preparation, the selection of suitable processing techniques, and the design of a model capable of recognising the individual states as accurately as possible.