AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Nano Research Article
PDF (45 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

A self-powered triboelectric nano-sensor enabled digital twin for self-sustained machine monitoring in smart mine

Jianping Jiang1,4, Chengliang Fan3, Hongyu Chen5, Fan Wu1,4, Xihui Feng3, Canjun Xiao2, Hongye Pan1,4, Xiaoping Wu1,4( ), Zutao Zhang1,2( )
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Chengdu Technological University, Chengdu 611730, China
School of Information Science & Technology, Southwest Jiaotong University, Chengdu 611756, China
Yibin Research Institute, Southwest Jiaotong University, Yibin 644000, China
School of Design, Southwest Jiaotong University, Chengdu 611756, China
Show Author Information

Graphical Abstract

A self-powered triboelectric nano-sensor based on vibrational balls mechanism is designed to monitor vibrations from different directions. The potential of using the proposed self-powered triboelectric nano-sensor (STNS) to construct a self-sustained smart mine digital twin ecosystem at a lower cost is demonstrated.

Abstract

Effective monitoring of mining machinery is of great significance. Sensor nodes, which form the basis of the mine’s digital twin system, often face issues of poor sustainability. Therefore, this study introduces a self-powered triboelectric nano-sensor (STNS) enabled digital twin for self-sustained machine monitoring in smart mine. The STNS is designed with three mutually perpendicular sensor units to ensure responsiveness to vibrational energy sources from different directions. Compared to conventional spring-assisted triboelectric nanogenerator (TENG) structures, it exhibits higher frequency adaptability and bandwidth. For a 2 mm amplitude, the STNS responds to frequencies above 10 Hz, with a frequency linearity error rate of less than 0.05%. Utilizing deep learning, the STNS detects various vibrational parameters with an accuracy of ±1 Hz for frequency and ±1 mm for amplitude. A real-time monitoring system based on a deep learning model was constructed and successfully demonstrated for real-time monitoring of amplitude, frequency, and tilt angle. With STNS installed on vibration motor, real-time recognition of the five operating states of the vibration motor and real-time digital twin monitoring were realized. By large-scale distributed deployment of STNS devices, a self-sustained smart mine digital twin ecosystem can be constructed at a lower cost.

Keywords

digital twinsmart minetriboelectric nanosensordeep learningvibration monitoring

Electronic Supplementary Material

Video
7287_ESM_Video S1.mp4
7287_ESM_Video S2.mp4
7287_ESM_Video S3.mp4
7287_ESM_Video S4.mp4
Download File(s)
7287_ESM.pdf (2.4 MB)

References

[1]

Ralston, J.; Reid, D.; Hargrave, C.; Hainsworth, D. Sensing for advancing mining automation capability: A review of underground automation technology development. Int. J. Min. Sci. Technol. 2014, 24, 305–310.
Crossref Google Scholar
[2]

Hazrathosseini, A.; Moradi Afrapoli, A. The advent of digital twins in surface mining: Its time has finally arrived. Resour. Policy 2023, 80, 103155.
Crossref Google Scholar
[3]

Bofill, J.; Abisado, M.; Villaverde, J.; Sampedro, G. A. Exploring digital twin-based fault monitoring: Challenges and opportunities. Sensors 2023, 23, 7087.
Crossref Google Scholar
[4]

Liu, M. N.; Fang, S. L.; Dong, H. Y.; Xu, C. Z. Review of digital twin about concepts, technologies, and industrial applications. J. Manuf. Syst. 2021, 58, 346–361.
Crossref Google Scholar
[5]

Adu-Manu, K. S.; Adam, N.; Tapparello, C.; Ayatollahi, H.; Heinzelman, W. Energy-harvesting wireless sensor networks (EH-WSNs): A review. ACM Trans. Sens. Netw. 2018, 14, 1–50.
Crossref Google Scholar
[6]

Wang, B. C.; Zhou, H. Y.; Li, X. Y.; Yang, G.; Zheng, P.; Song, C.; Yuan, Y. X.; Wuest, T.; Yang, H. Y.; Wang, L. H. Human digital twin in the context of industry 5.0. Robot. Comput. Integr. Manuf. 2024, 85, 102626.
Crossref Google Scholar
[7]

Belfadel, A.; Hörl, S.; Tapia, R. J.; Politaki, D.; Kureshi, I.; Tavasszy, L.; Puchinger, J. A conceptual digital twin framework for city logistics. Comput. Environ. Urban Syst. 2023, 103, 101989.
Crossref Google Scholar
[8]

Wang, H.; Chen, X. W.; Jia, F.; Cheng, X. J. Digital twin-supported smart city: Status, challenges and future research directions. Expert Syst. Appl. 2023, 217, 119531.
Crossref Google Scholar
[9]

Hao, D. N.; Gong, Y. C.; Wu, J. Y.; Shen, Q. H.; Zhang, Z. T.; Zhi, J. Y.; Zou, R.; Kong, W. H.; Kong, L. J. A self-sensing and self-powered wearable system based on multi-source human motion energy harvesting. Small 2024, 20, 2311036.
Crossref Google Scholar
[10]

Zhou, S. Z.; Li, Z. Y.; Zhou, S. X. A two-degree-of-freedom nonlinear electromagnetic energy harvester in rotational motion. Mech. Syst. Signal Process. 2024, 220, 111695.
Crossref Google Scholar
[11]

Hao, D. N.; Kong, L. J.; Zhang, Z. T.; Kong, W. H.; Tairab, A. M.; Luo, X.; Ahmed, A.; Yang, Y. W. An electromagnetic energy harvester with a half-wave rectification mechanism for military personnel. Sustain. Energy Technol. Assess. 2023, 57, 103184.
Crossref Google Scholar
[12]

Nadertehrani, A.; Ziaei-Rad, S.; Eshtehardiha, R. Harvesting vibration energy by quad-stable piezoelectric cantilever beam: Modeling, fabrication and testing. Eur. J. Mech. A/Solids 2024, 107, 105389.
Crossref Google Scholar
[13]

Sezer, N.; Koç, M. A comprehensive review on the state-of-the-art of piezoelectric energy harvesting. Nano Energy 2021, 80, 105567.
Crossref Google Scholar
[14]

Lin, Z. W.; Sun, C. C.; Zhang, G. Q.; Fan, E. D.; Zhou, Z. H.; Shen, Z. Y.; Yang, J.; Liu, M. Y.; Xia, Y. S.; Si, S. B. et al. Flexible triboelectric nanogenerator toward ultrahigh-frequency vibration sensing. Nano Res. 2022, 15, 7484–7491.
Crossref Google Scholar
[15]

Tang, S.; Chang, W. X.; Li, G.; Sun, J. F.; Du, Y.; Hui, X. D.; Tang, Q.; Hu, Z. H.; Li, J. Q.; Chen, J. et al. High performance wide frequency band triboelectric nanogenerator based on multilayer wave superstructure for harvesting vibration energy. Nano Res. 2023, 16, 6933–6939.
Crossref Google Scholar
[16]

Wang, K. K.; Yao, Y. M.; Liu, Y. X.; Guan, X.; Yu, Y.; Wang, J. L.; Wang, Y. Q.; Li, T. Y.; Cheng, T. H. Self-powered system for real-time wireless monitoring and early warning of UAV motor vibration based on triboelectric nanogenerator. Nano Energy 2024, 129, 110012.
Crossref Google Scholar
[17]

Tan, D. G.; Zhou, J. X.; Wang, K.; Zhao, X. H.; Wang, Q.; Xu, D. L. Bow-type bistable triboelectric nanogenerator for harvesting energy from low-frequency vibration. Nano Energy 2022, 92, 106746.
Crossref Google Scholar
[18]

Zhao, Y. C.; Gao, S. H.; Zhang, X.; Huo, W. X.; Xu, H.; Chen, C.; Li, J.; Xu, K. X.; Huang, X. Fully flexible electromagnetic vibration sensors with annular field confinement origami magnetic membranes. Adv. Funct. Mater. 2020, 30, 2001553.
Crossref Google Scholar
[19]

Chen, X. X.; Guo, H.; Wu, H. X.; Chen, H. T.; Song, Y.; Su, Z. M.; Zhang, H. X. Hybrid generator based on freestanding magnet as all-direction in-plane energy harvester and vibration sensor. Nano Energy 2018, 49, 51–58.
Crossref Google Scholar
[20]

Koka, A.; Sodano, H. A. High-sensitivity accelerometer composed of ultra-long vertically aligned barium titanate nanowire arrays. Nat. Commun. 2013, 4, 2682.
Crossref Google Scholar
[21]

Wang, Z. L.; Chen, J.; Lin, L. Progress in triboelectric nanogenerators as a new energy technology and self-powered sensors. Energy Environ. Sci. 2015, 8, 2250–2282.
Crossref Google Scholar
[22]

Xu, Y. H.; Yang, W. X.; Yu, X.; Li, H. C.; Cheng, T. H.; Lu, X. H.; Wang, Z. L. Real-time monitoring system of automobile driver status and intelligent fatigue warning based on triboelectric nanogenerator. ACS Nano 2021, 15, 7271–7278.
Crossref Google Scholar
[23]

Fang, Z.; Kong, L. J.; Chen, J. F.; Chen, H. Y.; Zhao, X. Y.; Luo, D. B. Zhang, Z. T. A multi-node self-powered fault detection system by triboelectric-electromagnetic nanosensors for smart transportation. Nano Energy 2024, 128, 109882.
Crossref Google Scholar
[24]

Fang, Z.; Zhou, Z. J.; Yi, M. Y.; Zhang, Z. T.; Luo, X.; Ahmed, A. A roller-bearing-based triboelectric nanosensor for freight train synergistic maintenance in smart transportation. Nano Energy 2023, 106, 108089.
Crossref Google Scholar
[25]

Zhang, C. G.; Liu, Y. B.; Zhang, B. F.; Yang, O.; Yuan, W.; He, L. X.; Wei, X. L.; Wang, J.; Wang, Z. L. Harvesting wind energy by a triboelectric nanogenerator for an intelligent high-speed train system. ACS Energy Lett. 2021, 6, 1490–1499.
Crossref Google Scholar
[26]

Jin, T.; Sun, Z. D.; Li, L.; Zhang, Q.; Zhu, M. L.; Zhang, Z. X.; Yuan, G. J.; Chen, T.; Tian, Y. Z.; Hou, X. Y. et al. Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications. Nat. Commun. 2020, 11, 5381.
Crossref Google Scholar
[27]

Zhang, X. Q.; Yu, M.; Ma, Z. R.; Ouyang, H.; Zou, Y.; Zhang, S. L.; Niu, H. K.; Pan, X. X.; Xu, M. Y.; Li, Z. et al. Self-powered distributed water level sensors based on liquid-solid triboelectric nanogenerators for ship draft detecting. Adv. Funct. Mater. 2019, 29, 1900327.
Crossref Google Scholar
[28]

Zhang, Q. Y.; Barri, K.; Kari, S. R.; Wang, Z. L.; Alavi, A. H. Multifunctional triboelectric nanogenerator-enabled structural elements for next generation civil infrastructure monitoring systems. Adv. Funct. Mater. 2021, 31, 2105825.
Crossref Google Scholar
[29]

Lee, D.; Kim, D. Dual output from unitary input for a hybrid coaxial triboelectric nanogenerator inspired by a crank engine. Nano Energy 2020, 71, 104599.
Crossref Google Scholar
[30]

Wang, X. H.; Yin, G. F.; Sun, T.; Xu, X. J.; Rasool, G.; Abbas, K. Mechanical vibration energy harvesting and vibration monitoring based on triboelectric nanogenerators. Energy Technol. 2024, 12, 2300931.
Crossref Google Scholar
[31]

Wang, C.; Zhang, X. S.; Wu, J.; Yu, X.; Cheng, T. H.; Ma, H. W.; Wang, Z. L. Double-spring-piece structured triboelectric sensor for broadband vibration monitoring and warning. Mech. Syst. Signal Process. 2022, 166, 108429.
Crossref Google Scholar
[32]

Tang, G.; Cheng, F.; Hu, X.; Huang, B.; Xu, B.; Li, Z. B.; Yan, X. X.; Yuan, D. D.; Wu, W. J.; Shi, Q. F. A two-degree-of-freedom cantilever-based vibration triboelectric nanogenerator for low-frequency and broadband operation. Electronics 2019, 8, 1526.
Crossref Google Scholar
[33]

Pang, Y. K.; Li, X. H.; Chen, M. X.; Han, C. B.; Zhang, C.; Wang, Z. L. Triboelectric nanogenerators as a self-powered 3D acceleration sensor. ACS Appl. Mater. Interfaces 2015, 7, 19076–19082.
Crossref Google Scholar
[34]

Jin, L.; Deng, W. L.; Su, Y. C.; Xu, Z.; Meng, H.; Wang, B.; Zhang, H. P.; Zhang, B. B.; Zhang, L.; Xiao, X. B. et al. Self-powered wireless smart sensor based on maglev porous nanogenerator for train monitoring system. Nano Energy 2017, 38, 185–192.
Crossref Google Scholar
[35]

Shi, Q.; Yang, J. H.; Gui, H.; Gui, Y. G.; Tang, C.; Yu, Y. Q.; Zhao, Z. Y. Self-powered circular-honeycomb triboelectric nanogenerator for vibration energy harvesting and resonance detection of synchronous machine. Sens. Actuators A Phys. 2023, 354, 114291.
Crossref Google Scholar
[36]

Xiao, X.; Zhang, X. Q.; Wang, S. Y.; Ouyang, H.; Chen, P. F.; Song, L. G.; Yuan, H. C.; Ji, Y. L.; Wang, P. H.; Li, Z. et al. Honeycomb structure inspired triboelectric nanogenerator for highly effective vibration energy harvesting and self-powered engine condition monitoring. Adv. Energy Mater. 2019, 9, 1902460.
Crossref Google Scholar
[37]

Zhao, H. F.; Shu, M. R.; Ai, Z. H.; Lou, Z. R.; Sou, K. W.; Lu, C. Y.; Jin, Y. C.; Wang, Z. H.; Wang, J. Y.; Wu, C. S. et al. A highly sensitive triboelectric vibration sensor for machinery condition monitoring. Adv. Energy Mater. 2022, 12, 2201132.
Crossref Google Scholar
[38]

Bhatta, T.; Pradhan, G. B.; Shrestha, K.; Jeong, S. H.; Zhang, S. P.; Kim, H. S.; Park, J. Y. All elastomeric pillars-based triboelectric vibration sensor for self-powered broad range machinery condition monitoring. Nano Energy 2023, 117, 108929.
Crossref Google Scholar
[39]

Xin, C. F.; Xu, Z. F.; Xie, X.; Guo, H. Y.; Peng, Y.; Li, Z. J.; Liu, L. L.; Xie, S. R. Structure-crack detection and digital twin demonstration based on triboelectric nanogenerator for intelligent maintenance. Adv. Sci. 2023, 10, 2302443.
Crossref Google Scholar
[40]

Yang, Y. Q.; Shi, Q. F.; Zhang, Z. X.; Shan, X. C.; Salam, B.; Lee, C. Robust triboelectric information-mat enhanced by multi-modality deep learning for smart home. InfoMat 2023, 5, e12360.
Crossref Google Scholar
[41]

Walter, P. L. Review: Fifty years plus of accelerometer history for shock and vibration (1940-1996). Shock Vib. 1999, 6, 197–207.
Crossref Google Scholar
[42]

Son, J. D.; Niu, G.; Yang, B. S.; Hwang, D. H.; Kang, D. S. Development of smart sensors system for machine fault diagnosis. Expert Syst. Appl. 2009, 36, 11981–11991.
Crossref Google Scholar
[43]

Wang, Z. L. Triboelectric nanogenerators as new energy technology for self-powered systems and as active mechanical and chemical sensors. ACS Nano 2013, 7, 9533–9557.
Crossref Google Scholar
[44]

Chen, J.; Zhu, G.; Yang, W. Q.; Jing, Q. S.; Bai, P.; Yang, Y.; Hou, T. C.; Wang, Z. L. Harmonic-resonator-based triboelectric nanogenerator as a sustainable power source and a self-powered active vibration sensor. Adv. Mater. 2013, 25, 6094–6099.
Crossref Google Scholar
[45]

Huang, C. M.; Li, Q. T.; Li, J.; Guo, H. Z.; Hao, W. H.; Sheng, K. X.; An, Y. S.; Chen, J. Y.; Zhang, X. Y.; Xu, M. Y. Research on dynamics of bouncing ball in triboelectric nanogenerator. J. Micromech. Microeng. 2021, 31, 085002.
Crossref Google Scholar
[46]

Luo, A. C. J.; Han, R. P. S. The dynamics of a bouncing ball with a sinusoidally vibrating table revisited. Nonlinear Dyn. 1996, 10, 1–18.
Crossref Google Scholar
[47]

Li, G. B.; Li, Q. T.; Hao, W. H.; Li, J.; Guo, H. Z.; Zheng, X. Q.; Huang, C. M. Numerical calculation and experimental investigation on optimization of bouncing ball triboelectric nanogenerator. Nano Energy 2022, 104, 107864.
Crossref Google Scholar
[48]

Niu, S. M.; Liu, Y.; Chen, X. Y.; Wang, S. H.; Zhou, Y. S.; Lin, L.; Xie, Y. N.; Wang, Z. L. Theory of freestanding triboelectric-layer-based nanogenerators. Nano Energy 2015, 12, 760–774.
Crossref Google Scholar
[49]

Ahmad, M. M.; Khan, N. M.; Khan, F. U. Review of frequency up-conversion vibration energy harvesters using impact and plucking mechanism. Int. J. Energy Res. 2021, 45, 15609–15645.
Crossref Google Scholar
[50]

Yu, Y.; Li, H. Y.; Zhao, D.; Gao, Q.; Li, X.; Wang, J. L.; Wang, Z. L.; Cheng, T. H. Material’s selection rules for high performance triboelectric nanogenerators. Mater. Today 2023, 64, 61–71.
Crossref Google Scholar
[51]

Lu, C. X.; Han, C. B.; Gu, G. Q.; Chen, J.; Yang, Z. W.; Jiang, T.; He, C.; Wang, Z. L. Temperature effect on performance of triboelectric nanogenerator. Adv. Eng. Mater. 2017, 19, 1700275.
Crossref Google Scholar
Nano Research
Volume 18 Issue 4,
April 2025
Article number: 94907287
DOI: 10.26599/NR.2025.94907287
Cite this article:
Jiang J, Fan C, Chen H, et al. A self-powered triboelectric nano-sensor enabled digital twin for self-sustained machine monitoring in smart mine. Nano Research, 2025, 18(4): 94907287. https://doi.org/10.26599/NR.2025.94907287

260

Views

34

Downloads

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics
Received: 15 November 2024
Revised: 04 January 2025
Accepted: 06 February 2025
Published: 03 April 2025
© The Author(s) 2025. Published by Tsinghua University Press.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).
Return