Squat Detection of Railway Switches and Crossings Using Point Machine Vibration Measurements

Zuo, Yang; Lundberg, Jan; Najeh, Tawfik; Rantatalo, Matti; Odelius, Johan

doi:10.3390/s23073666

Cited by 4 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The testbed used a complete S&C and a bogie of 6 tonnes. The setup is similar to previous studies by Zuo et al [39] and Zuo et al [42] The whole test site is visualised in Figure 1. The bogie used has two axles with a distance of 2.5 m in between.…”

Section: The Testbed and Experiments Setupmentioning

confidence: 82%

Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning

et al. 2023

Self Cite

View full text Add to dashboard Cite

Switches and crossings (S&Cs) are also known as turnouts or railway points. They are important assets in railway infrastructures and a defect in such a critical asset might lead to a long delay for the railway network and decrease the quality of service. A squat is a common rail head defect for S&Cs and needs to be detected and monitored as early as possible to avoid costly emergent maintenance activities and enhance both the reliability and availability of the railway system. Squats on the switchblade could even potentially cause the blade to break and cause a derailment. This study presented a method to collect and process vibration data at the point machine with accelerometers on three axes to extract useful features. The two most important features, the number of peaks and the total power, were found. Three different unsupervised machine learning algorithms were applied to cluster the data. The results showed that the presented method could provide promising features. The k-means and the agglomerative hierarchical clustering methods are suitable for this data set. The density-based spatial clustering of applications with noise (DBSCAN) encounters some challenges.

show abstract

Section: The Testbed and Experiments Setupmentioning

confidence: 82%

Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…These frequencies bands are characteristic frequencies of the track and can depend on a number of factors, including the position of a squat relative to the closest sleepers [10]. This, to some extent, also works in the experiment setup [38,39] for frequencies around 300 Hz. These studies are solemnly aimed at squats, and differentiation toward other types of impact event origins or the overlap of various impact events at the same time is not discussed.…”

Section: Comparisonmentioning

confidence: 99%

“…In [ 38 ], the authors develop a squat detection algorithm for the whole switch based on wavelets, time domain features and isolation forest. Further investigations into squat detection using wavelets are carried out in [ 39 ]. However, other transient impact sources such as joints and intersections are also detected without differentiation.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Local Track Discontinuities and Defects in Railway Switches Based on Track-Side Accelerations

Reetz,

Najeh,

Lundberg

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

Switches are an essential, safety-critical part of the railway infrastructure. Compared to open tracks, their complex geometry leads to increased dynamic loading on the track superstructure from passing trains, resulting in high maintenance costs. To increase efficiency, condition monitoring methods specific to railway switches are required. A common approach to track superstructure monitoring is to measure the acceleration caused by vehicle track interaction. Local interruptions in the wheel–rail contact, caused for example by local defects or track discontinuities, appear in the data as transient impact events. In this paper, such transient events are investigated in an experimental setup of a railway switch with track-side acceleration sensors, using frequency and waveform analysis. The aim is to understand if and how the origins of these impact events can be distinguished in the data of this experiment, and what the implications for condition monitoring of local track discontinuities and defects with wayside acceleration sensors are in practice. For the same experimental configuration, individual impact events are shown to be reproducible in waveform and frequency content. Nevertheless, with this track-side sensor setup, the different types of track discontinuities and defects (squats, joints, crossing) could not be clearly distinguished using characteristic frequencies or waveforms. Other factors, such as the location of impact event origin relative to the sensor, are shown to have a much stronger influence. The experimental data suggest that filtering the data to narrow frequency bands around certain natural track frequencies could be beneficial for impact event detection in practice, but differentiating between individual impact event origins requires broadband signals. A multi-sensor setup with time-synchronized acceleration sensors distributed over the switch is recommended.

show abstract

“…The drawback is that if not all trains have the ABA measurement systems installed, defects with faster failure development time could be missed while the trains with no measurement system occupy the track. On the other hand, by introducing vibration sensors mounted in the S&C, monitoring of the S&C for each train passage could be performed [32]. Timely detection of S&C squats is crucial for ensuring the safe and reliable operation of railway systems.…”

Section: Introductionmentioning

confidence: 99%

“…Timely detection of S&C squats is crucial for ensuring the safe and reliable operation of railway systems. Several studies have been performed to explore the feasibility of measuring the vibrations from an accelerometer mounted on the rod of a point machine [20,32]. These studies provide promising results.…”

Section: Introductionmentioning

confidence: 99%

Wayside Railway Switch and Crossing Monitoring Using Isolation Forest Anomaly Scores

Zuo,

Chandran,

Odelius

et al. 2023

Sustainability

Self Cite

View full text Add to dashboard Cite

Railway switch and crossing (S&C) systems have complicated moving structures compared with regular rail. They require multiple components that vary in complexity. The complexity of railway S&C, together with the fact that they are discontinuous points of the system, makes them vulnerable to defects such as squats. A squat on the switching rail could potentially cause rail breakage and lead to catastrophic results, such as derailment. In this study, a method based on anomaly scoring was investigated to estimate the status of an S&C system with respect to squat defects. The proposed method was tested in a real environment under controlled measurement sequences. The results show that the methods can differ between an S&C with squats and another one without them.

show abstract

Squat Detection of Railway Switches and Crossings Using Point Machine Vibration Measurements

Cited by 4 publications

References 29 publications

Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning

Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning

Analysis of Local Track Discontinuities and Defects in Railway Switches Based on Track-Side Accelerations

Wayside Railway Switch and Crossing Monitoring Using Isolation Forest Anomaly Scores

Contact Info

Product

Resources

About