Self-Powered Wireless Sensor Node for Smart Railway Axle Box Bearing via a Variable Reluctance Energy Harvesting System

Gong, Yun; Wang, Sijia; Xie, Zhengqiu; Zhang, Tao; Chen, Wenqiang; Lü, Xingjie; Zeng, Qiang; Gao, Yuhan; Huang, Wenbin

doi:10.1109/tim.2021.3076857

Cited by 18 publications

(9 citation statements)

References 22 publications

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“…Combined with equations ( 9) and (11), the relationship between the wind speed and the angular velocity of the RD-TENG output is given by:…”

Section: Performancementioning

confidence: 99%

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Rotation differential triboelectric nanogenerator for bird-repellent on transmission line towers

Zhang,

Lin,

Huang

et al. 2023

Smart Mater. Struct.

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The security of transmission line towers is crucial to industrial development. Bird damage is a significant threat to transmission line towers in the field. However, the power supply to the bird-repellent devices is limited. In this work, we propose a rotation differential triboelectric nanogenerator (RD-TENG) to harvest wind energy for supplying electrical power to bird-repellent on transmission line towers. On the transmission line towers, the RD-TENG converts wind energy into electrical energy by rotation differential structure and stores power in a 1000 μF capacitor, then supplies power to the buzzer for bird-repellent. The device mainly consists of a wind cup, an input gear, output gears, and rotors. The RD-TENG can achieve double rotation speed by rotating the differential structure compared to the single gear counterpart, hence doubling the current and power output through triboelectric nanogenerator. The RD-TENG has a starting wind speed of 4 m s−1 and can operate normally at lower wind speeds. At a wind speed of 8 m s−1, the output performance of the RD-TENG is 830 V, 13.5 μA, 330 nC, and the peak power is 19.6 mW. This work realizes a new solution for bird-repellent on transmission lines through the TENG energy harvesting technique and also contributes to the subsequent smart grid construction.

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“…Combined with equations ( 9) and (11), the relationship between the wind speed and the angular velocity of the RD-TENG output is given by:…”

Section: Performancementioning

confidence: 99%

“…Energy harvesting technology provides insights into the aforementioned problem [8][9][10][11]. In 2012, an energy harvesting method that combines triboelectrification effect and electrostatic induction was first proposed by Wang's group [12][13][14][15], namely triboelectric nanogenerator (TENG).…”

Section: Introductionmentioning

confidence: 99%

Rotation differential triboelectric nanogenerator for bird-repellent on transmission line towers

Zhang,

Lin,

Huang

et al. 2023

Smart Mater. Struct.

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“…A vast area of research is focused on the implementation of VREHs to optimize the output power [108][109]. In [110], the authors proposed and embedded VREH to power an integrated WSN in the railways. Surprisingly, the output power has reached around 80mW, which makes it suitable for feeding a wide range of wireless sensors including, speed, vibration, strain, and temperature measurement devices.…”

Section: E Variable Reluctancementioning

confidence: 99%

Review and Analysis of Magnetic Energy Harvesters: A Case Study for Vehicular Applications

Enayati

Asef

2022

IEEE Access

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Energy harvesting (EH), as an enabling technology of energy derivation from ambient sources, has attracted much research attention in wireless sensor network (WSN) context. The magnetic energy harvester (MEH) introduces ambient energy harvesters' most promising technological development. This paper presents a review and analysis of MEH applications in WSN like vehicular systems and technologies. The successful approaches are introduced and classified based on technical characteristics and working principles. The fundamentals of their operation are discussed in detail, and the power points of each method are reviewed. To select the optimal energy harvester, in this work a case study is provided for feeding navigational sensors mounted on a rotating wheel of vehicles. Finally, the performance of the developed MEH model is evaluated and discussed for harvesting energy at the rotating wheels of a ground vehicle. To offer electromagnetic field analysis of the studied MEH, the simulations are performed in Ansys Maxwell software for further harvested power evaluation.

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“…Intelligent is the development trend of modern industry. Under this background, the development of high-end bearings is inevitably inseparable from the intelligent trend, and intelligent bearings emerge at the historic moment [4], [5]. The intelligent bearing is based on the traditional bearing integrated with different sensors, microprocessor, and control system, so that the combination into one and form a unique bearing structure unit.…”

Section: Introductionmentioning

confidence: 99%

Bearings Intelligent Fault Diagnosis by 1-D Adder Neural Networks

Tang

Wei

et al. 2022

JDMD

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Integrated with sensors, processors and RF communication modules, intelligent bearing could achieve the autonomous perception and autonomous decision-making, guarantying the safety and reliability during their use. However, because of the resource limitations of the end device, processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network (CNN), which involves a great amount of multiplicative operations. To minimize the computation cost of the conventional CNN, based on the idea of AdderNet, a 1-D adder neural network with a wide first-layer kernel (WAddNN) suitable for bearing fault diagnosis is proposed in this paper. The proposed method uses the l1-norm distance between filters and input features as the output response, thus making the whole network almost free of multiplicative operations. The whole model takes the original signal as the input, uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise, then uses two layers of small kernels for nonlinear mapping. Through experimental comparison with CNN models of the same structure, WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost. The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources.

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Self-Powered Wireless Sensor Node for Smart Railway Axle Box Bearing via a Variable Reluctance Energy Harvesting System

Cited by 18 publications

References 22 publications

Rotation differential triboelectric nanogenerator for bird-repellent on transmission line towers

Rotation differential triboelectric nanogenerator for bird-repellent on transmission line towers

Review and Analysis of Magnetic Energy Harvesters: A Case Study for Vehicular Applications

Bearings Intelligent Fault Diagnosis by 1-D Adder Neural Networks

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