Derailment Detection and Data Collection in Freight Trains, Based on a Wireless Sensor Network

Macucci, Massimo; Pascoli, Stefano Di; Marconcini, Paolo; Tellini, Bernardo

doi:10.1109/tim.2016.2556925

Cited by 25 publications

(6 citation statements)

References 17 publications

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“…In those monitoring scenarios such as wagon location and security, mechanical vibration energy generated by the interaction of train wheels and rails is the most promising source in this case to power railway wagon monitoring sensors (RWMSs) based on the Internet of Things [4]- [5]. Generally, there are two main mechanisms to convert the mechanical vibration energy into the electrical energy: piezoelectric [6]- [8] and electromagnetic principle [6], [9].…”

Section: Introductionmentioning

confidence: 99%

Geophone-Based Energy Harvesting Approach for Railway Wagon Monitoring Sensor With High Reliability and Simple Structure

Shi

et al. 2020

IEEE Access

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Freight train positioning and axle safety monitoring are the key to railway traffic supervision, which can be achieved by sensors based on Internet of Things (IoT) technology, and the energy harvesting technology is the best solution to those sensors due to neither electrical power nor communication system is available on railway wagons. This paper proposes a self-powered railway wagon monitoring sensor (SpRWMS) solution based on the geophone, which has the characteristics of mature technology and simple structure, and can convert mechanical vibration of wagons into electrical energy. Since the vibration is so weak that the voltage induced by the geophone is very weak, hence, this paper proposes an electromagnetic generator (EMG) constructed by geophone matrix and an energy management interface (EMI) to convert the induced potential into the voltage that can drive the circuit system of sensors. In this paper, the implementation and performance of EMI will be focused on, and the energy harvesting system analysis prior to developing prototypes and conducting field trail. The design principles and prototypes of EMG and EMI are proposed based on the parameters of vibration profiles collected by a data logger, and the performance of the SpRWMS prototype is evaluated on a trial run freight train. Experimental results show that the proposed EMI can extract the energy generated by EMG more effectively and increase the voltage across the storage capacitor from 2V to 3.3V, that is, our proposed EMI and EMG can convert the railway wagon vibrational energy into the electrical energy form required by the circuitry of sensors. INDEX TERMS Energy harvesting, Internet of Things, vibration.

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Section: Introductionmentioning

confidence: 99%

Geophone-Based Energy Harvesting Approach for Railway Wagon Monitoring Sensor With High Reliability and Simple Structure

Shi

et al. 2020

IEEE Access

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“…During such an event, long tracks of the railroad can be damaged before the failure is noticed by the train driver. This delay in response is rooted to the length of the train and the high noise from railroad locomotives and wagons [4].…”

Section: Introductionmentioning

confidence: 99%

“…However, most ore freight cars still use pneumatic braking control valves [7,8]. These cars do not have power sources in any of the wagons [4,9]. To address this problem, power supplies need to be provided to the desired monitoring sensors, both to power the equipment and to charge the batteries.…”

Section: Introductionmentioning

confidence: 99%

Optimizing strain energy extraction from multi-beam piezoelectric devices for heavy haul freight cars

Lopes

Eckert

Martins

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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Heavy haul trains used to transport commodities are generally very long and operated by a single individual. Owing to high noise levels from the rolling stock displacement, the driver cannot notice failures in the compartments. It is practically impossible to visually inspect the wagons located far from the locomotive. Sensors that can measure in-train forces may improve the train's operation, indicating potential failures and preventing accidents and derailments. However, the freight cars employed in most of the railroads are not equipped with electric power sources, making inspections unlikely. Therefore, an energy harvesting system must be developed for the sensors to avoid the need for periodic battery charging or replacement. As a high level of kinetic energy is present in the rolling stock, a vibration energy harvester (VEH) system is an acceptable alternative. Among VEH devices, multi-beam piezoelectric materials used in planar zigzag (PZ) or orthogonal spiral outer fixed (OSo) configurations can be effective alternatives. These can be powered by low-frequency strains readily available in the freight cars. This study investigates the optimum geometries of PZ and OSo aiming to increase the generated power and minimize the structure mass with a focus on their application to ore wagons. Among the optimum solutions, the OSo geometry generates the maximum power, up to 20.93 mW, which is sufficient to feed some critical devices. Conversely, the PZ configurations present the highest energy density, up to 16.595 mW/kg(m s −2) 2 , which allows for a more suitable solution to be combined in serial and/or parallel pack configurations.

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“…O vagão ferroviário é um dos ativos mais importantes de uma ferrovia, uma vez que pode ser empregado tanto no transporte de cargas quanto no transporte de pessoas. Tamanha importância é reetida na necessidade de uma severa inspeção dos componentes que os formam, a m de, principalmente, prevenir acidentes [Macucci et al 2016].…”

Section: Introductionunclassified

Image Inspection of Railcar Structural Components: An approach through Deep Learning and Discrete Fourier Transform

Rocha¹,

Silva²,

Gomes³

et al. 2019

Anais Do VII Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2019)

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Railcar components inspection is one of the most critical tasks in railway maintenance. The use of image processing, coupled with machine learning, has emerged as a solution for replacing current standard methodologies. The spectral analysis gives the frequency representation of a signal and has been largely used in signal processing tasks. In this sense, this work proposes the evaluation of the use of the Discrete Fourier Transform (DFT) in addition to the spatial representation image of railcar components for an automatic detector of defective parts performed by Convolutional Neural Network (CNN) classi cation. The results are given in measures of accuracy, precision, recall, and F1-score metrics in addition to the accuracy boxplot, and showed that the use of the DFT increase in 1.04% the CNN classi cation accuracy.

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Derailment Detection and Data Collection in Freight Trains, Based on a Wireless Sensor Network

Cited by 25 publications

References 17 publications

Geophone-Based Energy Harvesting Approach for Railway Wagon Monitoring Sensor With High Reliability and Simple Structure

Geophone-Based Energy Harvesting Approach for Railway Wagon Monitoring Sensor With High Reliability and Simple Structure

Optimizing strain energy extraction from multi-beam piezoelectric devices for heavy haul freight cars

Image Inspection of Railcar Structural Components: An approach through Deep Learning and Discrete Fourier Transform

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