Train overload and unbalanced load detection based on FBG gauge

Pan, Jianjun; Li, Weilai; Dai, Xin

doi:10.1117/12.2031546

Cited by 5 publications

(3 citation statements)

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“…Pan et al 46 designed a structure for installing the FBG sensor to increase the sensitivity of the vertical wheel-rail force measurement. For achieving this purpose, they positioned the FBG sensor in the centre of a thin steel gauge and suspended the fibre from its two ends.…”

Section: W1 ¼ P1 ð6þmentioning

confidence: 99%

“…This sensitive coefficient is valuable for calibrating the measured FBG wavelength which is used for detecting the dynamic wheel load. 46 Ultrasonic technique. Salzburger et al 47 proposed a wayside and in-service monitoring system to evaluate the wheels, for finding surface cracks, based on ultrasonic inspection.…”

Section: W1 ¼ P1 ð6þmentioning

confidence: 99%

“…compact size, independence from electric power in the measurement point; 10 . great accuracy and sensitivity, stability in spite of ambient temperature change, and corrosion resistance; 46 . ability of using only one end of the fibre for interrogating the data.…”

Section: Contact Pressure Distributionmentioning

confidence: 99%

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Condition monitoring approaches for the detection of railway wheel defects

Alemi

Corman

Lodewijks

2016

Proceedings of the Institution of Mechanical Engineers, Part F:

110

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Condition monitoring systems are commonly exploited to assess the health status of equipment. A fundamental part of any condition monitoring system is data acquisition. Meaningfully estimating the current condition and predicting the future behaviour of the equipment strongly depends on the characteristic of the data measurement stage. Nowadays, condition monitoring has wide applications in the railway industry and various monitoring approaches have been proposed for the inspection of wheel and rail conditions. In-service condition monitoring of wheels provides the real-time data required for maintenance planning, while in-workshop inspection is normally done at fixed intervals carried out periodically. In-service data acquisition can be divided into on-board and wayside measurements. In this paper, on the basis of these classifications, the existing data acquisition techniques for the monitoring of railway wheel condition are reviewed, and the state-of-the-art methods and required research are discussed.

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Section: W1 ¼ P1 ð6þmentioning

confidence: 99%

Section: W1 ¼ P1 ð6þmentioning

confidence: 99%

See 1 more Smart Citation

Condition monitoring approaches for the detection of railway wheel defects

Alemi

Corman

Lodewijks

2016

Proceedings of the Institution of Mechanical Engineers, Part F:

110

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Time series data mining for railway wheel and track monitoring: a survey

Lourenço,

Ribeiro,

Fernandes

et al. 2024

Neural Comput & Applic

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The railway sector has witnessed a significant surge in condition-based maintenance, thanks to the proliferation of sensing technologies and data-driven methodologies, such as machine learning. However, despite the plethora of algorithms designed to detect and classify track irregularities and wheel out-of-roundness, they often fall short when put to the test in real-world scenarios. These shortcomings typically stem from their inability to meet all four critical requirements for constructing an effective maintenance plan: (R1) suitability of the condition-based maintenance strategy, (R2) availability of relevant data, (R3) proper problem formulation, and (R4) accurate evaluation of data mining methods. In response to the absence of a unified framework and standardized guidelines, this survey delves into the realm of time series sensor data and wheel-track interface components for railway structural health monitoring. This survey aims to bridge this gap by offering an extensive categorization, pinpointing existing challenges, and outlining potential directions for future research. Through these efforts, this survey provides a more thorough and targeted exploration of the subject matter, contributing to the advancement of this field.

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