ArticleSensors (Basel, Switzerland)2022
Squat Detection of Railway Switches and Crossings Using Wavelets and Isolation Forest.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation.Sensors (Basel, Switzerland) · 2024Article
- Analysis of Local Track Discontinuities and Defects in Railway Switches Based on Track-Side Accelerations.Sensors (Basel, Switzerland) · 2024Article
- Squat Detection of Railway Switches and Crossings Using Point Machine Vibration Measurements.Sensors (Basel, Switzerland) · 2023Article
- Fusing Expert Knowledge with Monitoring Data for Condition Assessment of Railway Welds.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Railway switches and crossings (S&Cs) are critical, high-value assets in railway networks. A single failure of such an asset could result in severe network disturbance and considerable economical losses. Squats are common rail surface defects of S&Cs and need to be detected and estimated at an early stage to minimise maintenance costs and increase the reliability of S&Cs. For practicality, installation of wired or wireless sensors along the S&C may not be reliable due to the risk of damages of power and signal cables or sensors. To cope with these issues, this study presents a method for collecting and processing vibration data from an accelerometer installed at the point machine to extract features related to the squat defects of the S&C. An unsupervised anomaly-detection method using the isolation forest algorithm is applied to generate anomaly scores from the features. Important features are ranked and selected. This paper describes the procedure of parameter tuning and presents the achieved anomaly scores. The results show that the proposed method is effective and that the generated anomaly scores indicate the health status of an S&C regarding squat defects.
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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.