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Generalising AI model performance for Non Destructive Testing in railway systems
Citation Link: https://doi.org/10.15480/882.13521
Publikationstyp
Conference Paper
Date Issued
2024-09-18
Sprache
English
Author(s)
TORE-DOI
Start Page
34
End Page
41
Citation
35. Forum Bauinformatik, fbi 2024: 34-41
Contribution to Conference
Publisher
Technische Universität Hamburg, Institut für Digitales und Autonomes Bauen
Peer Reviewed
true
Maintaining railway systems is an essential task during operation of such public infrastructure, but the methods have remained unchanged in Germany for decades. Advancements in ML modeling, Simulation Techniques, and Data Management enable possibilities to accelerate the shift to more efficiently digitized evaluation processes. This research aims to analyze the capabilities of generalization of a previously identified Deep Learning model, trained on real-field ultrasonic data from regular inspection runs, and augmented by simulated defects. As this cannot be done by the usually applied summary statistics, it is necessary to analyze the model performance following the questions of how well the model learns data pattern explicitly, can interpolate between the trained parameters, and whether it did not overfit on specific simulation pattern. This is done by applying selective sampling strategies and an analysis of interesting results. The findings indicate a good fit for explicit patterns with an AUC of 0.964, while interpolation between parameters is successful only in specific use cases. Finally, we conclude that the model likely learns background patterns from simulations and may not necessarily apply simulated defects to real-world scenarios.
Subjects
Machine Learning
NDE
Railway Maintenance
Sensor Data
DDC Class
625: Road and Railroad
004: Computer Sciences
681: Precision Instruments and Other Devices
620: Engineering
Publication version
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Generalising AI model performance for Non Destructive Testing in railway systems.pdf
Type
Main Article
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333.41 KB
Format
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