The AIFRI Project - Artificial Intelligence For Rail Inspection
- The rails of modern railways face an enormous wear and tear from ever increasing train speeds and loads. This necessitates diligent non-destructive testing for defects of the entire railway system. Non-destructive testing of rail tracks is carried out by rail inspection trains equipped with ultrasonic and eddy current test devices. However, the evaluation of the gathered data is mainly done manually with a strong focus on ultrasonic data, and defects are checked on-site using hand-held testing equipment. Maintenance measures are derived based on these on-site findings. The aim of the AIFRI project (Artificial Intelligence For Rail Inspection) is to - increase the degree of automation of the inspection process, from the evaluation of the data to the planning of maintenance measures, - increase the accuracy of defect detection, - automatically classify detected indications into risk classes. These aims will be achieved by training a neural network for defect detection andThe rails of modern railways face an enormous wear and tear from ever increasing train speeds and loads. This necessitates diligent non-destructive testing for defects of the entire railway system. Non-destructive testing of rail tracks is carried out by rail inspection trains equipped with ultrasonic and eddy current test devices. However, the evaluation of the gathered data is mainly done manually with a strong focus on ultrasonic data, and defects are checked on-site using hand-held testing equipment. Maintenance measures are derived based on these on-site findings. The aim of the AIFRI project (Artificial Intelligence For Rail Inspection) is to - increase the degree of automation of the inspection process, from the evaluation of the data to the planning of maintenance measures, - increase the accuracy of defect detection, - automatically classify detected indications into risk classes. These aims will be achieved by training a neural network for defect detection and classification. Since the current testing data is unbalanced, insufficiently labeled and largely unverified we will supplement fused, simulated eddy current and ultrasonic testing data in form of a configurable digital twin.…
Autor*innen: | Alexander Friedrich |
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Weitere Person: | Thomas Heckel, Ralf CaspersonORCiD, Tianyun Zhang, G. Olm, A. Islam, A. Simroth |
Dokumenttyp: | Vortrag |
Veröffentlichungsform: | Präsentation |
Sprache: | Englisch |
Jahr der Erstveröffentlichung: | 2022 |
Organisationseinheit der BAM: | 8 Zerstörungsfreie Prüfung |
8 Zerstörungsfreie Prüfung / 8.4 Akustische und elektromagnetische Verfahren | |
DDC-Klassifikation: | Naturwissenschaften und Mathematik / Chemie / Analytische Chemie |
Freie Schlagwörter: | Artificial intelligence; Eddy current; Non-destructive testing; Ultrasound Simulation |
Themenfelder/Aktivitätsfelder der BAM: | Chemie und Prozesstechnik |
Veranstaltung: | PostDoc Day 2022 |
Veranstaltungsort: | Berlin, Germany |
Beginndatum der Veranstaltung: | 03.11.2022 |
Enddatum der Veranstaltung: | 03.11.2022 |
Verfügbarkeit des Dokuments: | Datei im Netzwerk der BAM verfügbar ("Closed Access") |
Datum der Freischaltung: | 28.03.2023 |
Referierte Publikation: | Nein |
Eingeladener Vortrag: | Nein |