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Sensors 2018, 18(5), 1603;

Effective Crack Detection in Railway Axles Using Vibration Signals and WPT Energy

Mechanical Engineering Department,Universidad Carlos III de Madrid, 28911 Madrid, Spain
Author to whom correspondence should be addressed.
Received: 22 April 2018 / Revised: 10 May 2018 / Accepted: 14 May 2018 / Published: 17 May 2018
(This article belongs to the Special Issue Smart Sensing System for Real-Time Monitoring)
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Crack detection for railway axles is key to avoiding catastrophic accidents. Currently, non-destructive testing is used for that purpose. The present work applies vibration signal analysis to diagnose cracks in real railway axles installed on a real Y21 bogie working on a rig. Vibration signals were obtained from two wheelsets with cracks at the middle section of the axle with depths from 5.7 to 15 mm, at several conditions of load and speed. Vibration signals were processed by means of wavelet packet transform energy. Energies obtained were used to train an artificial neural network, with reliable diagnosis results. The success rate of 5.7 mm defects was 96.27%, and the reliability in detecting larger defects reached almost 100%, with a false alarm ratio lower than 5.5%. View Full-Text
Keywords: bogies test rig; condition monitoring; crack detection; vibration analysis bogies test rig; condition monitoring; crack detection; vibration analysis

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Gómez, M.J.; Corral, E.; Castejón, C.; García-Prada, J.C. Effective Crack Detection in Railway Axles Using Vibration Signals and WPT Energy. Sensors 2018, 18, 1603.

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