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Title:

Reinforcement Learning for Structural Health Monitoring based on Inspection Data

Document type:
Konferenzbeitrag
Author(s):
Simon Pfingstl, Yann Niklas Schoebel, Markus Zimmermann
Pages contribution:
203-210
Abstract:
Due to uncertainty associated with fatigue, mechanical structures have to be often inspected, especially in aerospace. In order to reduce inspection effort, fatigue behavior can be predicted based on measurement data and supervised learning methods, such as neural networks or particle filters. For good predictions, much data is needed. However, often only a small number of sensors to collect data are available, e.g., on airplanes due to weight limitations. This paper presents a method where data...     »
Keywords:
reinforcement learning, structural health monitoring, crack growth, inspection timing
Book / Congress title:
8th Asia-Pacific Workshop on Structural Health Monitoring
Volume:
18
Publisher:
Materials Research Forum LLC
Year:
2021
Covered by:
Scopus
Fulltext / DOI:
doi:https://doi.org/10.21741/9781644901311-24
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