ArticleSensors (Basel, Switzerland)2018
Effective Crack Detection in Railway Axles Using Vibration Signals and WPT Energy.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Detecting Excitations of Pipes, Ropes, and Bars Using Piezo Sensors and Collecting Information Remotely.Sensors (Basel, Switzerland) · 2025Article
- Railway Axle Early Fatigue Crack Detection through Condition Monitoring Techniques.Sensors (Basel, Switzerland) · 2023Article
- New support roller profile design for railway wheel re-profiling process by under-floor lathes with a single cutting tool.Scientific reports · 2022Article
- Wavelet Scattering and Neural Networks for Railhead Defect Identification.Materials (Basel, Switzerland) · 2021Article
- Monitoring the Structural Health of Glass Fibre-Reinforced Hybrid Laminates Using Novel Piezoceramic Film.Sensors (Basel, Switzerland) · 2020Article
- Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines.Sensors (Basel, Switzerland) · 2020Article
- Detecting Debonding between Steel Beam and Reinforcing CFRP Plate Using Active Sensing with Removable PZT-Based Transducers.Sensors (Basel, Switzerland) · 2019Article
- A Novel Method for Identifying Crack and Shaft Misalignment Faults in Rotor Systems under Noisy Environments Based on CNN.Sensors (Basel, Switzerland) · 2019Article
- Feature Extraction from Indirect Monitoring in Marine Oil Separation Systems.Sensors (Basel, Switzerland) · 2018Article
- Early Fault Diagnosis for Planetary Gearbox Based Wavelet Packet Energy and Modulation Signal Bispectrum Analysis.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.