Detection and evaluation of rail surface defects using alternating current field measurement techniques

Papaelias, Mayorkinos; Lugg, Martin

doi:10.1177/0954409712444840

Cited by 17 publications

(12 citation statements)

References 13 publications

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“…It can be difficult to interpret the information received during inspection, that is, to determine the relationship between the signals received from inspection hardware and the size of the defects under test. This task is nearly always complicated by the presence of clusters of multiple closely-spaced cracks compared to isolated cracks [16,17]. Following the fatal and economically damaging Hatfield, UK, rail derailment [18] inspection of the British railway network revealed more than 2000 sites of potentially serious RCF cracks [19].…”

Section: Introductionmentioning

confidence: 99%

Characterisation of clustered cracks using an ACFM sensor and application of an artificial neural network

Rowshandel

Nicholson

Shen

et al. 2018

NDT & E International

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The alternating current field measurement (ACFM) technique can be applied for surface-breaking fatigue crack detection and sizing; the link between the ACFM signal and crack size is well understood for individual cracks. However, the ACFM response to multiple clustered cracks is significantly different to that of isolated cracks. In railway rails the high wheel-rail forces can lead to rolling contact fatigue (RCF) cracks. Often cracks appear together in small clusters or in long stretches. The accurate characterisation of such fatigue cracks is essential for carrying out efficient and safe repair and maintenance. This paper presents a method for sizing the important sub-surface section of multiple cracks using ACFM via the application of an artificial neural network (ANN). The approach is demonstrated using a railway case study: a simulation-based dataset of signal response covering the range of RCF cracks typically seen in in-service railway tracks has been generated to give a thorough representation of the effect of clustered crack parameters on the ACFM response. A 5×5×2×1 multi-layer ANN has been optimised and trained using the validated simulation database to learn the inverse relationship between the crack pocket length (desired output) and the ACFM signal for a given cluster of RCF cracks. The network has been evaluated on a set of experimental data to size cracks of known dimensions from ACFM measurements and also on unseen simulation data. Results from both simulation and experiment show that the approach presented can be used to size clustered cracks to approximately the same degree of accuracy as is possible for isolated cracks.

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Section: Introductionmentioning

confidence: 99%

Characterisation of clustered cracks using an ACFM sensor and application of an artificial neural network

Rowshandel

Nicholson

Shen

et al. 2018

NDT & E International

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“…However, this method, only reliably detects severe defects with cracks deeper than 5 mm. Other methods, such as visual inspection [7], alternating current field [8], image recognition [9], strain gage instrumented wheel [10], acoustic detection [11], and guided-wave-based health monitoring [12], have been proposed, each of which has different advantages in terms of cost, reliability, and robustness. This paper focuses on the use of axle box acceleration (ABA) measurements.…”

Section: Introductionmentioning

confidence: 99%

Improvements in Axle Box Acceleration Measurements for the Detection of Light Squats in Railway Infrastructure

Molodova

Núñez

et al. 2015

IEEE Trans. Ind. Electron.

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Squats are a type of short-wave rolling contact fatigue defect whose early detection can contribute to cost reductions in the railway industry. This paper demonstrates how the early detection of squats is possible via enhanced instrumentation based on axle box acceleration (ABA) and adequate postprocessing. Three improvements are discussed. The first corresponds to the use of longitudinal ABA to enhance measurement sensitivity to light squats. Compared to vertical ABA, longitudinal ABA does not contain the vibrations of the rail, fastening, sleepers, and ballast, and thus, the impact-related vibration is considerably stronger in the signal. The second improvement considers the use of multiple sensors, noise-reduction techniques, and repeated measurements. Due to hunting, the wheels of a measuring train do not always pass over small squats; thus, light squats are more likely to be detected using multiple sensors and multiple measurement runs. The third improvement concerns the signal-processing solution for the reduction of disturbances from wheel defects. Extensive field measurements show that these improvements make the characteristics of squats more visible in signals and allow the squats to be distinguished from vibrations of other origins.

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“…Consequently, most degraded crossings are reactively replaced when they reach severe degradation. The limitations of manual inspection motivate the development of on-board monitoring technologies, such as ultrasonic measurement [ 4 , 5 ], eddy current testing [ 6 , 7 ], magnetic induction [ 8 ], image recognition [ 9 , 10 , 11 ], vibration-based inspection [ 12 , 13 ], guided-wave inspection [ 14 ], radio detection and ranging sensors [ 15 , 16 ], thermography [ 17 , 18 ], acoustic emission systems [ 19 , 20 ] and ground-penetrating radar [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Degradation at Railway Crossings Using Axle Box Acceleration Measurements

Wei

Núñez

et al. 2017

Sensors

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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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Detection and evaluation of rail surface defects using alternating current field measurement techniques

Cited by 17 publications

References 13 publications

Characterisation of clustered cracks using an ACFM sensor and application of an artificial neural network

Characterisation of clustered cracks using an ACFM sensor and application of an artificial neural network

Improvements in Axle Box Acceleration Measurements for the Detection of Light Squats in Railway Infrastructure

Evaluating Degradation at Railway Crossings Using Axle Box Acceleration Measurements

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