Anomaly detection in railway infrastructure
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2020
Typ der Arbeit
Master
Studiengang
Sammlung
Typ
11 - Studentische Arbeit
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
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Verlag / Herausgebende Institution
Hochschule für Wirtschaft FHNW
Verlagsort / Veranstaltungsort
Olten
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
This thesis elaborates the topic of artificial intelligence used for anomaly detection in railway infrastructure. In Switzerland, railways are the backbone of the public transport system and an important factor in the economy and society. Therefore, it is important to detect unwanted anomalies in railway infrastructure as fast as possible and before they result in an incident, whereby even a minor interruption can evolve into a major disturbance. Some of the challenges arising from this field can be met with artificial intelligence, especially with machine learning techniques and knowledge engineering. Railway infrastructures offer a wide range of potential in anomaly detection, since they are complex systems. Especially sub-systems, which are exposed to forces (e.g. acceleration or deceleration, rotating or moving) which can result in material wear and maintenance effort, provide promising use cases for anomaly detection. During the research, one particular problem was identified: The arc ignition in the pantograph-catenary system during train operation. Frequent arc ignition will accelerate attrition, respectively, the faster loss of material results in more frequent maintenance or malfunctioning, which eventually leads to a higher idle time or an interruption in operations. The literature review has shown that little effort was expended to solve the problem of arc ignition detection in the pantograph-catenary system....
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Begutachtung
Open Access-Status
Lizenz
Zitation
MORANDI, David, 2020. Anomaly detection in railway infrastructure. Olten: Hochschule für Wirtschaft FHNW. Verfügbar unter: https://irf.fhnw.ch/handle/11654/40348