Detection of Bolt Tightness of Fish-plated Joints using Axle Box Acceleration

Li, Z.; Oregui, M.; Carroll, R.; Li, S.; Moraal, Jan

doi:10.4203/ccp.98.84

Cited by 2 publications

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“…From the point of view of track maintenance, several authors have based their research on axlebox accelerations. They allow detecting and identifying some singular track defects such as squats [12][13][14][15][16], bolt tightness of fish-plated joints [17] and other short track defects [18,19]. They further serve as a basis for the calibration and validation of different wear prediction models [20].…”

Section: Introductionmentioning

confidence: 99%

“…In studies dedicated to short wavelength or singular track defects, sampling resolution can achieve high values, hence providing higher resolution and frequency content. In these cases, data may be recorded at a sampling frequency of 25.6 kHz at speeds lower than 40 km/h for tramway track monitoring purposes [17] or at speeds around 110 km/h for conventional tracks [20]. Despite such a high sampling frequency value, in this last case only frequencies up to 2 kHz are analysed.…”

Section: Introductionmentioning

confidence: 99%

“…However, as important as how to record axlebox accelerations is how to process and analyse them in order to extract the most information possible. Among other methods, the wavelet theory was applied for achieving a proper time-frequency characterisation of such accelerations [13,17,25,31]. Moreover, the wavelet theory was compared with the more conventional Fourier analysis, showing the advantages and drawbacks of each method [32].…”

Section: Introductionmentioning

confidence: 99%

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Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

et al. 2016

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Railway track maintenance is becoming a real challenge for Railway Engineers due to the need of meeting increasingly high quality requirements by means of cost-effective procedures. Frequently, this can be only achieved by implementing some technological developments from other fields into the railway sector, such as Digital Signal Processing. Indeed, the present work delves into data acquisition and processing techniques in order to enhance track surveying processes. For this purpose, run tests on the Metropolitan Rail Network of Valencia (Spain) were carried out, and axlebox accelerations were gathered and analysed in different ways. The results determined the optimal sampling and filtering frequencies as well as the location of accelerometers along the train. Furthermore, by means of spectral analysis and time-frequency representations, diverse track defects, track singularities and vibration modes can be clearly identified. It is shown how, with a Hamming time window of 0.5 s and an overlapping of 95%, a wide set of track defects can be detected, without the need of complementary analyses. These values yield the best results as they are a good compromise between time and frequency resolution and allow for appropriate pattern recognition of the corresponding track singularities and resonant frequencies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

et al. 2016

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“…Most NDE techniques have to be conducted offline to avoid interrupting normal rail service while getting to know the real-time conditions of rail infrastructure is one of the top concerns among rail operators and researchers. There are also some methods that utilize axle box accelerations from onboard monitoring systems to identify singular track defects such as squarts 12 and bolt tightness of fish-plated joints 13 or to detect rail corrugation. 14 Although these methods can cover a long distance, there may be a long interval between the inspections for a specific location.…”

Section: Introductionmentioning

confidence: 99%

An acoustic-homologous transfer learning approach for acoustic emission–based rail condition evaluation

Chen

Zhou

et al. 2020

Structural Health Monitoring

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This article presents a novel transfer learning approach for evaluating structural conditions of rail in a progressive manner, by using acoustic emission monitoring data and knowledge transferred from an acoustic-related database. Specifically, the low-level layers of a model pre-trained on large audio data are leveraged in our model for feature extraction. Compared with conventional transfer learning approaches that transfer knowledge from models pre-trained on normal images, the proposed approach can handle acoustic emission spectrograms more naturally in terms of both data inner properties and the way of data intaking. The training and testing data used for rail condition evaluation contains two months of acoustic emission recordings, which were acquired from an in situ operating rail turnout with an integrated acoustic emission –based monitoring system. Results show that the evolving stages of rail from intact to critically cracked are successfully revealed using the proposed approach with excellent prediction accuracy and high computation efficiency. More importantly, the study quantitatively shows that audio source data are more relevant to the acoustic emission monitoring data than Image data, by introducing a maximum mean discrepancy metric, and the transfer learning model with smaller maximum mean discrepancy does lead to better performance. As a by-product of the study, it has also been found that the features extracted by the proposed transfer learning model (“bottleneck” features) already exhibit a clustering trend corresponding to the evolving stages of rail conditions even in the training process before any label is annotated, indicating the potential unsupervised learning capability of the proposed approach. Through the study, it is suggested that selecting source data more correspondingly and flexibly would maximize the benefit of transfer learning in structural health monitoring area when facing heterogenous monitoring data under varying practical scenarios.

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Detection of Bolt Tightness of Fish-plated Joints using Axle Box Acceleration

Cited by 2 publications

References 0 publications

Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

An acoustic-homologous transfer learning approach for acoustic emission–based rail condition evaluation

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