ArticleSensors (Basel, Switzerland)2017
Evaluating Degradation at Railway Crossings Using Axle Box Acceleration Measurements.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 66 citations in OpenAlex.
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- Railway Axle Early Fatigue Crack Detection through Condition Monitoring Techniques.Sensors (Basel, Switzerland) · 2023Article
- Automated machine learning recognition to diagnose flood resilience of railway switches and crossings.Scientific reports · 2023Article
- Squat Detection of Railway Switches and Crossings Using Wavelets and Isolation Forest.Sensors (Basel, Switzerland) · 2022Article
- Fatigue damage assessment of complex railway turnout crossings via Peridynamics-based digital twin.Scientific reports · 2022Article
- Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach.Sensors (Basel, Switzerland) · 2021Article
- Measurement of the Geometric Center of a Turnout for the Safety of Railway Infrastructure Using MMS and Total Station.Sensors (Basel, Switzerland) · 2020Article
- Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines.Sensors (Basel, Switzerland) · 2020Article
- Data-Driven Bias Correction and Defect Diagnosis Model for In-Service Vehicle Acceleration Measurements.Sensors (Basel, Switzerland) · 2020Article
- Correlation Analysis and Verification of Railway Crossing Condition Monitoring.Sensors (Basel, Switzerland) · 2019Article
- Effective Crack Detection in Railway Axles Using Vibration Signals and WPT Energy.Sensors (Basel, Switzerland) · 2018Article
- EMD-Based Methodology for the Identification of a High-Speed Train Running in a Gear Operating State.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we investigate the capability of an axle box acceleration (ABA) system to evaluate the degradation at railway crossings. For this purpose, information from multiple sensors, namely, ABA signals, 3D rail profiles, Global Positioning System (GPS) and tachometer recordings, was collected from both nominal and degraded crossings. By proper correlation of the gathered data, an algorithm was proposed to distinguish the characteristic ABA related to the degradation and then to evaluate the health condition of crossings. The algorithm was then demonstrated on a crossing with an unknown degradation status, and its capability was verified via a 3D profile measurement. The results indicate that the ABA system is effective at monitoring two types of degradations. The first type is uneven deformation between the wing rail and crossing nose, corresponding to characteristic ABA frequencies of 230-350 and 460-650 Hz. The second type is local irregularity in the longitudinal slope of the crossing nose, corresponding to characteristic ABA frequencies of 460-650 Hz. The types and severity of the degradation can be evaluated by the spatial distribution and energy concentration of the characteristic frequencies of the ABA signals.
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