Monitoring of Rail-Wheel Interaction Using Acoustic Emission (AE)

Thakkar, N.A.; Steel, John Alexander; Reuben, R.L.; Knabe, G.; Dixon, D.; Shanks, R.L.

doi:10.4028/0-87849-420-0.161

Cited by 8 publications

(19 citation statements)

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“…The frequency bandwidth range of AE sensors between 150 kHz and 750 kHz was used. This paper clearly confirmed that the waveforms generated by AE were related to crack propagation in rail steel [83]. In field measurements, the rolling noise produced from wheel irregularities may affect the AE waveform which had similar amplitude as crack growth.…”

Section: Acoustic Emissionsupporting

confidence: 77%

State-of-the-Art Review of Railway Track Resilience Monitoring

2018

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Abstract:In recent years, railway systems have played a significant role in transportation systems due to the demand increase in conveying both cargo and passengers. Due to the harsh environments and severe loading conditions, caused by the traffic growth, heavier axles and vehicles and increase in speed, railway tracks are at risk of degradation and failure. Condition monitoring has been widely used to support the health assessment of civil engineering structures and infrastructures. In this context, it was adopted as a powerful tool for an objective assessment of the railway track behaviour by enabling real-time data collection, inspection and detection of structural degradation. According to relevant literature, a number of sensors can be used to monitor track behaviour during the train passing under harsh environments. This paper presents a review of sensors used for structural monitoring of railway track infrastructure, as well as their application to sense the performance of different track components during extreme events. The insight into track monitoring for railways serving traffic with extreme features will not only improve the track inspection and damage detection but also enable a predictive track maintenance regime in order to assist the decision-making process towards more cost-effective management in the railway industry.

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Section: Acoustic Emissionsupporting

confidence: 77%

State-of-the-Art Review of Railway Track Resilience Monitoring

2018

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“…A simple analytical model for the AE incident on the sensor was compared with measured data from a laboratory model (single wheel). In the same paper, Thakkar et al [26] cite similar slow wave speeds but higher damping (k ¼ 1.3) on a real standard British rail made of pearlitic steel and observations of AE from a field test for a real train passing in which signatures of individual wheels can be seen. The results presented by Thakkar et al and Shank shows that the general background envelope of the signals changed in a predictable way and that spikes in the signals were possibly related to features on the rail or wheel.…”

Section: Introductionmentioning

confidence: 74%

“…Previous work by Thakkar et al [26] presented preliminary studies using a test rig including a circular mild steel track prepared by cold rolling around which a scale model wheel was driven by an electric motor mounted at the centre of the track. The same test rig, with details and dimensions given in Table 1 and shown in Fig.…”

Section: Experimental Set Upmentioning

confidence: 99%

“…The same test rig, with details and dimensions given in Table 1 and shown in Fig. 1, was used here and the calibration results reported in [26] were used for the parameters of the analytical model. (Although lower amplitude (faster) wave also existed during calibration using pencil-lead breaks, its effect on the overall energy recorded at the sensor was small in comparison with the slower wave and that also attenuates faster and hence was not used to prepare numerical model.…”

Section: Experimental Set Upmentioning

confidence: 99%

“…[23,24]. Some preliminary studies of AE wave propagation from a moving wheel-rail interface have been reported by Thakkar et al [26], who used Hsu-Nielsen pencil-lead break tests to investigate the wave propagation and damping around a circular track using an array of 10 sensor positions. By carefully selecting threshold voltages and time windows to avoid effects of a fast wave and wave reflections, they identified a high amplitude slow wave speed of 3087 m/s and a damping coefficient (for AE energy) k ¼ 0.57 dB/m.…”

Section: Introductionmentioning

confidence: 96%

See 2 more Smart Citations

Rail–wheel interaction monitoring using Acoustic Emission: A laboratory study of normal rolling signals with natural rail defects

Thakkar

Steel

Reuben

2010

Mechanical Systems and Signal Processing

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A Bayesian Probabilistic Approach for Acoustic Emission‐Based Rail Condition Assessment

Wang

Liu

2017

Computer aided Civil Eng

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The investigation described in this article aims at developing a Bayesian‐based approach for probabilistic assessment of rail health condition using acoustic emission monitoring data. It comprises the following three phases: (i) formulation of a frequency‐domain structural health index (SHI), via a linear transformation method, tailored to damage‐sensitive frequency bandwidth; (ii) establishment of data‐driven reference models, using Bayesian regression about the real and imaginary parts of the SHI derived with monitoring data from the intact rail; and (iii) quantitative evaluation of discrimination between the new observations representative of current rail health condition and the baseline model predictions in terms of Bayes factor. If the deviation of the new observations from the predictions is within an acceptable tolerance, no damage is flagged, and the new data are further used to update and refine the reference models. If the observations deviate substantially from the model predictions in a probabilistic sense, damage is signaled, damage severity is quantified, and damage location determined. The proposed approach is examined by using field monitoring data acquired from an instrumented railway turnout, and the coincidence between the assessment results and the actual health conditions demonstrates its effectiveness in damage detection, localization, and quantification.

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Monitoring of Rail-Wheel Interaction Using Acoustic Emission (AE)

Cited by 8 publications

References 0 publications

State-of-the-Art Review of Railway Track Resilience Monitoring

State-of-the-Art Review of Railway Track Resilience Monitoring

Rail–wheel interaction monitoring using Acoustic Emission: A laboratory study of normal rolling signals with natural rail defects

A Bayesian Probabilistic Approach for Acoustic Emission‐Based Rail Condition Assessment

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