Abstract
Railway axles are safety-critical components, which are designed to be safe and reliable under standard operation. However, unexpected service conditions, impact events and issues missed in maintenance can affect their lifetime. Non-destructive testing (NDT) methods employed to assure structural integrity are expensive and disruptive requiring axles to be taken out of service for inspection. Therefore, there is need for more efficient, real-time monitoring approach. This paper presents an overview of key challenges facing successful in-service implementation of an acoustic emission (AE) condition monitoring system. This is followed by an experimental study of AE data captured during a mechanical test of a railway axle. The data is clustered using a self-organising map (SOM) to differentiate damage signals from other sources. The results presented show consistency with the crack growth measured using traditional NDT techniques.
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Acknowledgements
This work is funded by Innovate UK MONAXLE project, InnovateUK Grant 104243.
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Marks, R., Stancu, A., Soua, S. (2021). Fatigue Crack Growth Monitoring Using Acoustic Emission: A Case Study on Railway Axles. In: Gelman, L., Martin, N., Malcolm, A.A., (Edmund) Liew, C.K. (eds) Advances in Condition Monitoring and Structural Health Monitoring. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-9199-0_52
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DOI: https://doi.org/10.1007/978-981-15-9199-0_52
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