Triboelectric Nanogenerators for Condition Monitoring of Machines, Infrastructure and Environment

Mang Gao , Zhiyuan Yang , Yafeng Pang , Guozhang Dai , Chengkuo Lee , Junho Choi , Junliang Yang

Interdisciplinary Materials ›› 2025, Vol. 4 ›› Issue (5) : 645 -685.

PDF
Interdisciplinary Materials ›› 2025, Vol. 4 ›› Issue (5) : 645 -685. DOI: 10.1002/idm2.70004
REVIEW

Triboelectric Nanogenerators for Condition Monitoring of Machines, Infrastructure and Environment

Author information +
History +
PDF

Abstract

With the emergence of triboelectric nanogenerators (TENGs), the monitoring technology based on the triboelectric effect is becoming more and more popular due to the advantages of the wide selection of materials and flexible working modes. Traditional condition monitoring technologies for machines, infrastructure, and environment (MIE) are usually based on piezoelectric effects, thermal effects, and acoustic effects, which need external power to drive. The advancement of TENGs provides more possibilities to enable condition monitoring technologies with self-driving ability in the society of artificial intelligence of things (AIoT) systems. The flexible structure design and materials selection facilitate the condition monitoring of modern MIE in a more economical and effective way. An increasing number of related works are emerging. In these regards, this paper reviews the state of the art in condition monitoring based on TENGs for the applications of MIE and related interdisciplinary research, such as materials science, information, engineering, and so forth. The introduction of condition monitoring for MIE is illustrated and the basic mechanism of TENG is introduced first. Subsequently, the condition monitoring based on TENG technologies for machines, infrastructure, and environment is elucidated respectively. The most popular and hot research trends are pointed out and the current challenges are also discussed and illustrated, thus giving hints and guidance for future research trends.

Keywords

AIoT / condition monitoring / environment monitoring / infrastructure health monitoring / machinery monitoring / TENGs

Cite this article

Download citation ▾
Mang Gao, Zhiyuan Yang, Yafeng Pang, Guozhang Dai, Chengkuo Lee, Junho Choi, Junliang Yang. Triboelectric Nanogenerators for Condition Monitoring of Machines, Infrastructure and Environment. Interdisciplinary Materials, 2025, 4(5): 645-685 DOI:10.1002/idm2.70004

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

K. Rose, S. Eldridge, and L. Chapin, “The Internet of Things: An Overview,” Internet Society (ISOC) 80 (2015): 1-50.

[2]

C. Sheng, Z. Li, L. Qin, Z. Guo, and Y. Zhang, “Recent Progress on Mechanical Condition Monitoring and Fault Diagnosis,” Procedia Engineering 15 (2011): 142-146.

[3]

H. Barksdale, Q. Smith, and M. Khan, “Condition Monitoring of Electrical Machines With Internet of Things,” in SoutheastCon 2018 (IEEE, 2018), 1-4.

[4]

R. Srinivasan and A. K. Parlikad, “Value of Condition Monitoring in Infrastructure Maintenance,” Computers & Industrial Engineering 66 (2013): 233-241.

[5]

S. D. T. Kelly, N. K. Suryadevara, and S. C. Mukhopadhyay, “Towards the Implementation of IoT for Environmental Condition Monitoring in Homes,” IEEE Sensors Journal 13 (2013): 3846-3853.

[6]

G. Xu, Y. Shi, X. Sun, and W. Shen, “Internet of Things in Marine Environment Monitoring: A Review,” Sensors 19 (2019): 1711.

[7]

C. Cempel, Fundamentals of vibroacoustical condition monitoring, Handbook of Condition Monitoring: Techniques and Methodology (Springer, 1998), 324-353.

[8]

Y. Du, S. Zhou, X. Jing, Y. Peng, H. Wu, and N. Kwok, “Damage Detection Techniques for Wind Turbine Blades: A Review,” Mechanical Systems and Signal Processing 141 (2020): 106445.

[9]

G. Yu, M. Gao, and C. Jia, “A Fast Filtering Method Based on Adaptive Impulsive Wavelet for the Gear Fault Diagnosis,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 236 (2022): 1994-2008.

[10]

S. H. Yeo, L. P. Khoo, and S. S. Neo, “Tool Condition Monitoring Using Reflectance of Chip Surface and Neural Network,” Journal of Intelligent Manufacturing 11 (2000): 507-514.

[11]

S. Kurada and C. Bradley, “A Review of Machine Vision Sensors for Tool Condition Monitoring,” Computers in Industry 34 (1997): 55-72.

[12]

T. Sun, G. Yu, M. Gao, L. Zhao, C. Bai, and W. Yang, “Fault Diagnosis Methods Based on Machine Learning and Its Applications for Wind Turbines: A Review,” IEEE Access 9 (2021): 147481-147511.

[13]

S. Nithin, K. Hemanth, V. Shamanth, R. S. Mahale, P. Sharath, and A. Patil, “Importance of Condition Monitoring in Mechanical Domain,” Materials Today: Proceedings 54 (2022): 234-239.

[14]

M. Iliyas Ahmad, Y. Yusof, M. E. Daud, K. Latiff, A. Z. Abdul Kadir, and Y. Saif, “Machine Monitoring System: A Decade in Review,” International Journal of Advanced Manufacturing Technology 108 (2020): 3645-3659.

[15]

S. Uma and M. Parvathi, “BCD Adder Design Using New Reversible Logic for Low Power Applications,” Indian Journal of Science and Technology 10 (2017): 1-7.

[16]

M. G. Lipsett, Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the Second International Conference CMMNO’2012 (Springer, 2012), 99-107.

[17]

M. Tiboni, C. Remino, R. Bussola, and C. Amici, “A Review on Vibration-Based Condition Monitoring of Rotating Machinery,” Applied Sciences 12 (2022): 972.

[18]

D. Lee, K. H. Sun, B. Kim, and D. Kang, “Thermal Behavior of a Worn Tilting Pad Journal Bearing: Thermohydrodynamic Analysis and Pad Temperature Measurement,” Tribology Transactions 61 (2018): 1074-1083.

[19]

F. König, C. Sous, A. Ouald Chaib, and G. Jacobs, “Machine Learning Based Anomaly Detection and Classification of Acoustic Emission Events for Wear Monitoring in Sliding Bearing Systems,” Tribology International 155 (2021): 106811.

[20]

D. Ma, G. Lan, M. Hassan, W. Hu, and S. K. Das, “Sensing, Computing, and Communications for Energy Harvesting Iots: A Survey,” IEEE Communications Surveys & Tutorials 22 (2019): 1222-1250.

[21]

F. Pozo, D. A. Tibaduiza, and Y. Vidal, “New Electronic Tongue Sensor Array System for Accurate Liquor Beverage Classification,” Sensors 23, no. 13 (2023): 6178.

[22]

R. Joseph and V. Giurgiutiu, “Analytical and Experimental Study of Fatigue-Crack-Growth AE Signals in Thin Sheet Metals,” Sensors 20 (2020): 5835.

[23]

B. Zhang, S. Zhang, W. Li, et al., “Self-Powered Sensing for Smart Agriculture by Electromagnetic-Triboelectric Hybrid Generator,” ACS Nano 15 (2021): 20278-20286.

[24]

Z. L. Wang, “Triboelectric Nanogenerators as New Energy Technology for Self-Powered Systems and as Active Mechanical and Chemical Sensors,” ACS Nano 7 (2013): 9533-9557.

[25]

Q. Han, Z. Ding, Z. Qin, T. Wang, X. Xu, and F. Chu, “A Triboelectric Rolling Ball Bearing With Self-Powering and Self-Sensing Capabilities,” Nano Energy 67 (2020): 104277.

[26]

Q. Han, Z. Jiang, Y. Kong, and F. Chu, “Prebent Membrane-Based Disk-Type Triboelectric Nanogenerator Applied to Fault Diagnosis in Rotating Machinery,” IEEE/ASME Transactions on Mechatronics 27 (2022): 4686-4696.

[27]

W. Li, Y. Liu, S. Wang, et al., “Vibrational Triboelectric Nanogenerator-Based Multinode Self-Powered Sensor Network for Machine Fault Detection,” IEEE/ASME Transactions on Mechatronics 25 (2020): 2188-2196.

[28]

L. Jin, W. Deng, Y. Su, et al., “Self-Powered Wireless Smart Sensor Based on Maglev Porous Nanogenerator for Train Monitoring System,” Nano Energy 38 (2017): 185-192.

[29]

X. Yang, J. Han, F. Wu, et al., “A Novel Retractable Spring-Like-Electrode Triboelectric Nanogenerator With Highly-Effective Energy Harvesting and Conversion for Sensing Road Conditions,” RSC Advances 7 (2017): 50993-51000.

[30]

K. Xia, J. Liu, W. Li, et al., “A Self-Powered Bridge Health Monitoring System Driven by Elastic Origami Triboelectric Nanogenerator,” Nano Energy 105 (2023): 107974.

[31]

Y. Pang, S. Chen, J. An, et al., “Multilayered Cylindrical Triboelectric Nanogenerator to Harvest Kinetic Energy of Tree Branches for Monitoring Environment Condition and Forest Fire,” Advanced Functional Materials 30 (2020): 2003598.

[32]

C. Men, X. Liu, Y. Chen, S. Liu, S. Wang, and S. Gao, “Cotton-Assisted Dual Rotor-Stator Triboelectric Nanogenerator for Real-Time Monitoring of Crop Growth Environment,” Nano Energy 101 (2022): 107578.

[33]

X. Liang, S. Liu, Z. Ren, T. Jiang, and Z. L. Wang, “Self-Powered Intelligent Buoy Based on Triboelectric Nanogenerator for Water Level Alarming,” Advanced Functional Materials 32 (2022): 2205313.

[34]

W. Liu, X. Wang, Y. Song, et al., “Self-Powered Forest Fire Alarm System Based on Impedance Matching Effect Between Triboelectric Nanogenerator and Thermosensitive Sensor,” Nano Energy 73 (2020): 104843.

[35]

P. Jiang, L. Zhang, H. Guo, et al., “Signal Output of Triboelectric Nanogenerator at Oil-Water-Solid Multiphase Interfaces and Its Application for Dual-Signal Chemical Sensing,” Advanced Materials 31 (2019): e1902793.

[36]

H. Zhang, Y. Yang, X. Zhong, et al., “Single-Electrode-Based Rotating Triboelectric Nanogenerator for Harvesting Energy From Tires,” ACS Nano 8 (2014): 680-689.

[37]

J. Luo, Z. Wang, L. Xu, et al., “Flexible and Durable Wood-Based Triboelectric Nanogenerators for Self-Powered Sensing in Athletic Big Data Analytics,” Nature Communications 10 (2019): 5147.

[38]

M. Yang, J. Liu, C. Hu, et al., “Highly Sensitive Self-Powered Ammonia Gas Detection Enabled by a Rationally Designed Pani/Commercial Cellulosic Paper Based Triboelectric Nanogenerator,” Journal of Materials Chemistry A 11 (2023): 21937-21947.

[39]

J. Chen, J. Yang, Z. Li, et al., “Networks of Triboelectric Nanogenerators for Harvesting Water Wave Energy: A Potential Approach Toward Blue Energy,” ACS Nano 9 (2015): 3324-3331.

[40]

Z. Lin, B. Zhang, H. Guo, et al., “Super-Robust and Frequency-Multiplied Triboelectric Nanogenerator for Efficient Harvesting Water and Wind Energy,” Nano Energy 64 (2019): 103908.

[41]

J. An, Z. M. Wang, T. Jiang, X. Liang, and Z. L. Wang, “Whirling-Folded Triboelectric Nanogenerator With High Average Power for Water Wave Energy Harvesting,” Advanced Functional Materials 29 (2019): 1904867.

[42]

T. Jiang, H. Pang, J. An, et al., “Robust Swing-Structured Triboelectric Nanogenerator for Efficient Blue Energy Harvesting,” Advanced Energy Materials 10 (2020): 2000064.

[43]

J. Rao, Z. Chen, D. Zhao, et al., “Tactile Electronic Skin to Simultaneously Detect and Distinguish Between Temperature and Pressure Based on a Triboelectric Nanogenerator,” Nano Energy 75 (2020): 105073.

[44]

X. Rong, J. Zhao, H. Guo, et al., “Material Recognition Sensor Array by Electrostatic Induction and Triboelectric Effects,” Advanced Materials Technologies 5 (2020): 2000641.

[45]

Z. Ren, Z. Wang, Z. Liu, et al., “Energy Harvesting From Breeze Wind (0.7-6 M S −1) Using Ultra-Stretchable Triboelectric Nanogenerator,” Advanced Energy Materials 10 (2020): 2001770.

[46]

W. Ding, A. C. Wang, C. Wu, H. Guo, and Z. L. Wang, “Human-Machine Interfacing Enabled by Triboelectric Nanogenerators and Tribotronics,” Advanced Materials Technologies 4 (2019): 1800487.

[47]

X. Xie, X. Chen, C. Zhao, et al., “Intermediate Layer for Enhanced Triboelectric Nanogenerator,” Nano Energy 79 (2021): 105439.

[48]

W. Zhang, P. Wang, K. Sun, C. Wang, and D. Diao, “Intelligently Detecting and Identifying Liquids Leakage Combining Triboelectric Nanogenerator Based Self-Powered Sensor With Machine Learning,” Nano Energy 56 (2019): 277-285.

[49]

S. Chen, Y. Tang, M. Liu, L. Deng, L. Yang, and W. Zhang, “From Single-To Multi-Channel Systems: Advancing Handwriting Forgery Detection With Triboelectric Nanogenerator Arrays,” Nano Energy 139 (2025): 110925.

[50]

Z. L. Wang, “On Maxwell's Displacement Current for Energy and Sensors: The Origin of Nanogenerators,” Materials Today 20 (2017): 74-82.

[51]

Z. L. Wang, T. Jiang, and L. Xu, “Toward the Blue Energy Dream by Triboelectric Nanogenerator Networks,” Nano Energy 39 (2017): 9-23.

[52]

J. Luo and Z. L. Wang, “Recent Advances in Triboelectric Nanogenerator Based Self-Charging Power Systems,” Energy Storage Materials 23 (2019): 617-628.

[53]

C. Wu, A. C. Wang, W. Ding, H. Guo, and Z. L. Wang, “Triboelectric Nanogenerator: A Foundation of the Energy for the New Era,” Advanced Energy Materials 9 (2019): 1802906.

[54]

X. Xiao, X. Zhang, S. Wang, et al., “Honeycomb Structure Inspired Triboelectric Nanogenerator for Highly Effective Vibration Energy Harvesting and Self-Powered Engine Condition Monitoring, Advanced Energy,” Materials 9 (2019): 1902460.

[55]

M. Gao, Y. Li, and J. Choi, “Triboelectric Pad Journal Bearing for Self-Powered Condition Monitoring,” Nano Energy 103 (2022): 107851.

[56]

M. Gao, T. Sun, Y. Li, Z. Zhang, C. Lee, and J. Choi, “AI-Enabled Metal-Polymer Plain Bearing Based on the Triboelectric Principle,” Advanced Functional Materials 33 (2023): 2304070.

[57]

S. Li, D. Liu, Z. Zhao, et al., “A Fully Self-Powered Vibration Monitoring System Driven by Dual-Mode Triboelectric Nanogenerators,” ACS Nano 14 (2020): 2475-2482.

[58]

J. Ahn, J.-S. Kim, Y. Jeong, et al., “All-Recyclable Triboelectric Nanogenerator for Sustainable Ocean Monitoring Systems,” Advanced Energy Materials 12 (2022): 2201341.

[59]

Z. L. Wang, “Triboelectric Nanogenerators as New Energy Technology and Self-Powered Sensors-Principles, Problems and Perspectives,” Faraday Discussions 176 (2014): 447-458.

[60]

F. Xu, N. Ding, N. Li, et al., “A Review of Bearing Failure Modes, Mechanisms and Causes,” Engineering Failure Analysis 152 (2023): 107518.

[61]

F. Peng, L. Zheng, Y. Peng, C. Fang, and X. Meng, “Digital Twin for Rolling Bearings: A Review of Current Simulation and PHM Techniques,” Measurement 201 (2022): 111728.

[62]

A. Bewoor and S. Kulkarni, “Interoperability of International Standards, Condition Monitoring Methods and Research Models for Bearing Fault: An Integrated Approach,” Procedia Manufacturing 22 (2018): 982-989.

[63]

Z. Wu, T. Cheng, and Z. L. Wang, “Self-Powered Sensors and Systems Based on Nanogenerators,” Sensors 20 (2020): 2925.

[64]

Q. Zheng, Q. Tang, Z. L. Wang, and Z. Li, “Self-Powered Cardiovascular Electronic Devices and Systems,” Nature Reviews Cardiology 18 (2021): 7-21.

[65]

Q. Miao, C. Liu, N. Zhang, et al., “Toward Self-Powered Inertial Sensors Enabled by Triboelectric Effect,” ACS Applied Electronic Materials 2 (2020): 3072-3087.

[66]

S. Zhang, J. Guo, L. Liu, et al., “The Self-Powered Artificial Synapse Mechanotactile Sensing System by Integrating Triboelectric Plasma and Gas-Ionic-Gated Graphene Transistor,” Nano Energy 91 (2022): 106660.

[67]

L. Zhao, J. Guo, L. Liu, et al., “The Triboelectric Microplasma Transistor of Monolayer Graphene With a Reversible Oxygen Ion Floating Gate,” Nano Energy 78 (2020): 105229.

[68]

X. H. Li, C. B. Han, T. Jiang, C. Zhang, and Z. L. Wang, “A Ball-Bearing Structured Triboelectric Nanogenerator for Nondestructive Damage and Rotating Speed Measurement,” Nanotechnology 27 (2016): 085401.

[69]

X. S. Meng, H. Y. Li, G. Zhu, and Z. L. Wang, “Fully Enclosed Bearing-Structured Self-Powered Rotation Sensor Based on Electrification at Rolling Interfaces for Multi-Tasking Motion Measurement,” Nano Energy 12 (2015): 606-611.

[70]

D. Choi, T. Sung, and J. Y. Kwon, “A Self-Powered Smart Roller-Bearing Based on a Triboelectric Nanogenerator for Measurement of Rotation Movement,” Advanced Materials Technologies 3 (2018): 1800219.

[71]

Z. Xie, Y. Wang, R. Wu, et al., “A High-Speed and Long-Life Triboelectric Sensor With Charge Supplement for Monitoring the Speed and Skidding of Rolling Bearing,” Nano Energy 92 (2022): 106747.

[72]

Q. Han, Z. Jiang, X. Xu, Z. Ding, and F. Chu, “Self-Powered Fault Diagnosis of Rolling Bearings Based on Triboelectric Effect,” Mechanical Systems and Signal Processing 166 (2022): 108382.

[73]

J. Yang, Y. Sun, J. Zhang, B. Chen, and Z. L. Wang, “3D-printed Bearing Structural Triboelectric Nanogenerator for Intelligent Vehicle Monitoring, Cell Reports Physical,” Science 2 (2021): 100666.

[74]

Z. Xie, J. Dong, Y. Li, et al., “Triboelectric Rotational Speed Sensor Integrated into a Bearing: A Solid Step to Industrial Application,” Extreme Mechanics Letters 34 (2020): 100595.

[75]

S. Hu, T. Han, Y. Qi, C. Zhang, X. Shi, and Z. Peng, “Misalignment Fault Identification of a Multi-Span Rotor System Enabled by Triboelectric Nanogenerators,” Nano Energy 109 (2023): 108308.

[76]

M. Song, J. Chung, S.-H. Chung, et al., “Semisolid-Lubricant-Based Ball-Bearing Triboelectric Nanogenerator for Current Amplification, Enhanced Mechanical Lifespan, and Thermal Stabilization,” Nano Energy 93 (2022): 106816.

[77]

S. H. Chung, J. Chung, M. Song, et al., “Nonpolar Liquid Lubricant Submerged Triboelectric Nanogenerator for Current Amplification via Direct Electron Flow,” Advanced Energy Materials 11 (2021): 2100936.

[78]

Z. Li, X. Wang, T. Fu, et al., “Research on Nano-Film Composite Lubricated Triboelectric Speed Sensor for Bearing Skidding Monitoring,” Nano Energy 113 (2023): 108591.

[79]

J. Wang, B. Wu, G. Liu, et al., “Flexure Hinges Based Triboelectric Nanogenerator by 3D Printing,” Extreme Mechanics Letters 20 (2018): 38-45.

[80]

F. Dong, H. Yang, H. Du, et al., “Triboelectric Nanogenerator-Embedded Intelligent Bearing With Rolling Ball Defect Diagnosis via Signal Decomposition and Automated Machine Learning,” Nano Energy 119 (2024): 109072.

[81]

X. Fu, Z. Jiang, J. Cao, et al., “A Near-Zero Quiescent Power Breeze Wake-Up Anemometer Based on a Rolling-Bearing Triboelectric Nanogenerator,” Microsystems & Nanoengineering 10 (2024): 51.

[82]

L. Wang, F. Wang, L. Niu, X. Li, Z. Wang, and S. Yan, “Study on Generation Characteristics of a Triboelectric Ball Bearing With Defects,” Industrial Lubrication and Tribology 77 (2025): 341-348.

[83]

Z. L. Wang and A. C. Wang, “On the Origin of Contact-Electrification,” Materials Today 30 (2019): 34-51.

[84]

Z. Zhang, R. Yang, S. Deng, J. Liu, Q. Zhang, and C. Zhang, “Contact Electrification at Semiconductor Interfaces: The Tribovoltaic Effect,” MRS Bulletin 50 (2025): 336-346.

[85]

Y. Zi, J. Wang, S. Wang, et al., “Effective Energy Storage From a Triboelectric Nanogenerator,” Nature Communications 7 (2016): 10987.

[86]

Z. Zhang, N. Wu, L. Gong, R. Luan, J. Cao, and C. J. A. M. Zhang, “An Ultrahigh Power Density and Ultralow Wear GaN-Based Tribovoltaic Nanogenerator for Sliding Ball Bearing as Self-Powered Wireless Sensor Node,” Advanced Materials 36 (2024): 2310098.

[87]

S. Dong, T. Bu, Z. Wang, et al., “Freestanding-Mode Tribovoltaic Nanogenerator for Harvesting Sliding and Rotational Mechanical,” Energy 13 (2023): 2300079.

[88]

Z. Wang, K. Gao, Y. Feng, et al., “Characterizing Superlubricity by Tribovoltaic Effect,” Science Bulletin 69 (2024): 1197-1201.

[89]

L. Gong, Z. Zhang, W. Yu, et al., “Ultra-Durable Polysilicon Based Tribovoltaic Nanogenerators for Bearing In Situ Rotational Speed Sensing,” Small 20, no. 50 (2024): 2405992.

[90]

L. Gong, Z. Wang, R. Luan, et al., “Competitive Mechanism Between Interfacial Electric Field and Built-In Electric Field for Silicon-Based Tribovoltaic Effect,” Advanced Functional Materials 34, no. 10 (2024): 2310703.

[91]

F. Du, D. Li, X. Sa, et al., “Overview of Friction and Wear Performance of Sliding Bearings,” Coatings 12 (2022): 1303.

[92]

M. Chernets, M. Pashechko, A. Kornienko, and A. Buketov, “Study of the Influence of Temperature on Contact Pressures and Resource of Metal-Polymer Plain Bearings With Filled Polyamide PA6 Bushing,” Lubricants 10 (2022): 13.

[93]

Y. Ren, G. Liu, H. Yang, et al., “Dynamic Wear Sensor Array Based on Single-Electrode Triboelectric Nanogenerators,” Nano Energy 68 (2020): 104303.

[94]

Y. Feng, T. Jiang, X. Liang, J. An, and Z. L. Wang, “Cylindrical Triboelectric Nanogenerator Based on Swing Structure for Efficient Harvesting of Ultra-Low-Frequency Water Wave Energy,” Applied Physics Reviews 7 (2020): 021401.

[95]

M. Gao, S.-B. Kim, Y. Li, S. H. Ramaswamy, and J. Choi, “Triboelectric Nanogenerator With Enhanced Output and Durability Based on Si-DLC Films,” Nano Energy 105 (2022): 107997.

[96]

Y. Ra, S. Oh, J. Lee, et al., “Triboelectric Signal Generation and Its Versatile Utilization During Gear-Based Ordinary Power Transmission,” Nano Energy 73 (2020): 104745.

[97]

Z. Xie, Y. Wang, M. Yu, et al., “Triboelectric Sensor for Planetary Gear Fault Diagnosis Using Data Enhancement and CNN,” Nano Energy 103 (2022): 107804.

[98]

S. Wang, C. Zheng, T. Ma, et al., “Tooth Backlash Inspired Comb-Shaped Single-Electrode Triboelectric Nanogenerator for Self-powered Condition Monitoring of Gear Transmission,” Nano Energy 123 (2024): 109429.

[99]

S. Gao, R. Zhang, F. Wu, et al., “Rotating Single-Electrode Triboelectric V-belts With Skidding and Wear Monitoring Capabilities,” Tribology International 193 (2024): 109404.

[100]

S. Wang, L. Gong, Y. Jiang, et al., “Compound Motion-Mode Tribovoltaic Nanogenerator for Self-Powered Monitoring of Gear Transmission System,” Advanced Materials Technologies 10 (2025): 2401459.

[101]

X. Zhang, S. Wang, L. Gong, et al., “Ultra-Compact Single-Electrode Triboelectric Nanogenerators for Self-Powered Wear Sensing of Reciprocating Sealings,” Nano Energy 133 (2025): 110490.

[102]

Y. Shi, H. Li, X. Fu, et al., “Self-Powered Difunctional Sensors Based on Sliding Contact-Electrification and Tribovoltaic Effects for Pneumatic Monitoring and Controlling,” Nano Energy 110 (2023): 108339.

[103]

L.-C. Zhao, H.-X. Zou, Y.-J. Zhao, et al., “Hybrid Energy Harvesting for Self-Powered Rotor Condition Monitoring Using Maximal Utilization Strategy in Structural Space and Operation Process,” Applied Energy 314 (2022): 118983.

[104]

Y. Xin, T. Du, C. Liu, Z. Hu, P. Sun, and M. Xu, “A Ring-Type Triboelectric Nanogenerator for Rotational Mechanical Energy Harvesting and Self-Powered Rotational Speed Sensing,” Micromachines 13 (2022): 556.

[105]

F. P. Rad and A. Zabihollah, International Conference on MEMS (NANO and Smart Systems, 2012).

[106]

L. Zhang, F. Zhang, Z. Qin, et al., "Piezoelectric Energy Harvester for RollingBearings With Capability of Self-powered Condition Monitoring," Energy 238 (2022): 121770.

[107]

G. Zusman, “Universal Single Sensor for Machinery Condition Monitoring: Vibration, Bearing Health and Temperature,” in 19th Word Conference on Non-Destructive Testing (WCNDT, 2016), no.2016-07.2016.

[108]

I. Mehamud, P. Marklund, M. Björling, and Y. Shi, “Machine Condition Monitoring Enabled by Broad Range Vibration Frequency Detecting Triboelectric Nano-Generator (TENG)-Based Vibration Sensors,” Nano Energy 98 (2022): 107292.

[109]

C. Zhao, D. Liu, Y. Wang, et al., “Highly-Stretchable Rope-Like Triboelectric Nanogenerator for Self-Powered Monitoring in Marine Structures,” Nano Energy 94 (2022): 106926.

[110]

T. Du, X. Zuo, F. Dong, et al., “A Self-Powered and Highly Accurate Vibration Sensor Based on Bouncing-Ball Triboelectric Nanogenerator for Intelligent Ship Machinery Monitoring,” Micromachines 12 (2021): 218.

[111]

H. Zhao, M. Shu, Z. Ai, et al., “A Highly Sensitive Triboelectric Vibration Sensor for Machinery Condition Monitoring),” Advanced Energy Materials 12 (2022): 2270154.

[112]

N. Sarma, P. Tuohy, and S. Djurović, “Condition Monitoring of Rotating Electrical Machines.” in Encyclopedia of Electrical and Electronic Power Engineering, ed. J. García (Elsevier, 2023), 143-154.

[113]

A. Baldini, R. Felicetti, F. Ferracuti, A. Freddi, S. Iarlori, and A. Monteriù, “Real-Time Propeller Fault Detection for Multirotor Drones Based on Vibration Data Analysis,” Engineering Applications of Artificial Intelligence 123 (2023): 106343.

[114]

X. Gao, M. Huang, G. Zou, X. Li, and Y. Wang, “Self-Powered Vibration Sensor Based on the Coupling of Dual-Mode Triboelectric Nanogenerator and Non-Contact Electromagnetic Generator,” Nano Energy 111 (2023): 108356.

[115]

H. Luo, Y. Lu, Y. Xu, et al., “A Fully Soft, Self-Powered Vibration Sensor by Laser Direct Writing,” Nano Energy 103 (2022): 107803.

[116]

B. Zhang, L. Zhang, W. Deng, et al., “Self-Powered Acceleration Sensor Based on Liquid Metal Triboelectric Nanogenerator for Vibration Monitoring,” ACS Nano 11 (2017): 7440-7446.

[117]

T. Bhatta, G. B. Pradhan, K. Shrestha, et al., “All Elastomeric Pillars-Based Triboelectric Vibration Sensor for Self-Powered Broad Range Machinery Condition Monitoring,” Nano Energy 117 (2023): 108929.

[118]

S. Hosangadi Prutvi, M. Korrapati, and D. Gupta, “Self-Powering Vibration Sensor Based on a Cantilever System With a Single-Electrode Mode Triboelectric Nanogenerator,” Measurement Science and Technology 33 (2022): 075115.

[119]

R. Shen, S. He, Y. Luo, Z. He, Y. Gong, and G. Dai, “High-Sensitivity and High-Resolution Triboelectric Acoustic Sensor for Mechanical Equipment Monitoring,” Nano Energy 133 (2025): 110450.

[120]

G. Li, H. Wu, R. Guo, et al., “A Triboelectric Piston-Cylinder Assembly With Condition-Monitoring and Self-Powering Capabilities,” Energy Technology 10 (2022): 2200014.

[121]

Z. Qin, Y. Wang, Z. Yuan, D. Yu, and Z. Xie, “Triboelectric Linear Bearing Sensor for Self-Powered Condition Monitoring Using Wavelet Transform and Lightweight CNN,” Sensors and Actuators, A: Physical 359 (2023): 114455.

[122]

J. Zhao, Y. He, Y. Wang, W. Wang, L. Yan, and J. Luo, “An Investigation on the Tribological Properties of Multilayer Graphene and MoS2 Nanosheets as Additives Used in Hydraulic Applications,” Tribology International 97 (2016): 14-20.

[123]

J. Zhao, Y. Li, Y. He, and J. Luo, “In Situ Green Synthesis of the New Sandwichlike Nanostructure of Mn3O4/Graphene as Lubricant Additives,” ACS Applied Materials & Interfaces 11 (2019): 36931-36938.

[124]

J. Qu, W. C. Barnhill, H. Luo, et al., “Synergistic Effects Between Phosphonium-Alkylphosphate Ionic Liquids and Zinc Dialkyldithiophosphate (ZDDP) as Lubricant Additives,” Advanced Materials 27 (2015): 4767-4774.

[125]

S. Raadnui and S. Kleesuwan, “Low-Cost Condition Monitoring Sensor for Used Oil Analysis,” Wear 259 (2005): 1502-1506.

[126]

R.-H. Chen and C.-T. Chen, “Collision Between Immiscible Drops With Large Surface Tension Difference: Diesel Oil and Water,” Experiments in Fluids 41 (2006): 453-461.

[127]

E. Harika, M. Helene, J. Bouyer, and M. Fillon, “Impact of Lubricant Contamination With Water on Hydrodynamic Thrust Bearing Performance,” Mécanique & Industries 12 (2011): 353-359.

[128]

L. Du, J. Zhe, J. Carletta, R. Veillette, and F. Choy, “Real-Time Monitoring of Wear Debris in Lubrication Oil Using a Microfluidic Inductive Coulter Counting Device,” Microfluidics and Nanofluidics 9 (2010): 1241-1245.

[129]

H. Wu, T. Wu, Y. Peng, and Z. Peng, “Watershed-Based Morphological Separation of Wear Debris Chains for On-Line Ferrograph Analysis,” Tribology Letters 53 (2014): 411-420.

[130]

J. Zhao, D. Wang, F. Zhang, et al., “Real-Time and Online Lubricating Oil Condition Monitoring Enabled by Triboelectric Nanogenerator,” ACS Nano 15 (2021): 11869-11879.

[131]

J. Zhao, D. Wang, F. Zhang, et al., “Self-Powered, Long-Durable, and Highly Selective Oil-Solid Triboelectric Nanogenerator for Energy Harvesting and Intelligent Monitoring,” Nano-Micro Letters 14 (2022): 160.

[132]

K. Wang, Y. Sun, H. Zhang, Z. Ding, W. Song, and J. Li, “Self-Sensing Smart Thrust Roller Bearing Based on Triboelectric Nanogenerator With Highly Sensitivity for Monitoring Trace Contaminants in Lubricating Oil,” Nano Energy 119 (2024): 109058.

[133]

Y. Feng, X. Liu, Y. Lei, et al., “A Novel Self-Powered Triboelectric Sensor for Early Waring of Lubrication Failure,” Nano Energy 122 (2024): 109304.

[134]

L. Wang, J. Yu, P. Wang, et al., “Intelligent Online Sensing of Lubricating Oil Debris via Dual-Electrode Oil-Driven Triboelectric Nanogenerator,” Chemical Engineering Journal 503 (2025): 158413.

[135]

P. M. E. Cann, B. Damiens, and A. A. Lubrecht, “The Transition Between Fully Flooded and Starved Regimes in EHL,” Tribology International 37 (2004): 859-864.

[136]

M. L. Dumont, P. M. Lugt, and J. H. Tripp, “Surface Feature Effects in Starved Circular EHL Contacts,” Journal of Tribology 124 (2001): 358-366.

[137]

B. O. Ayegba, K.-J. I. Egbe, A. Matin Nazar, M. Huang, and M. A. Hariri-Ardebili, “Resource Efficiency and Thermal Comfort of 3D Printable Concrete Building Envelopes Optimized by Performance Enhancing Insulation: A Numerical Study,” Energies 15 (2022): 1069.

[138]

A. Matin Nazar, Y. Narazaki, A. Rayegani, and F. Rahimi Sardo, “Recent Progress of Triboelectric Nanogenerators as Self-Powered Sensors in Transportation Engineering,” Measurement 203 (2022): 112010.

[139]

C. S. Tumrate, D. K. Saini, P. Gupta, and D. Mishra, “Evolutionary Computation Modelling for Structural Health Monitoring of Critical Infrastructure,” Archives of Computational Methods in Engineering 30 (2022): 1479-1493.

[140]

A. H. Alavi and W. G. Buttlar, “An Overview of Smartphone Technology for Citizen-Centered, Real-Time and Scalable Civil Infrastructure Monitoring,” Future Generation Computer Systems 93 (2019): 651-672.

[141]

T. Al-Zuriqat, C. Chillón Geck, K. Dragos, and K. Smarsly, “Adaptive Fault Diagnosis for Simultaneous Sensor Faults in Structural Health Monitoring Systems,” Infrastructures 8 (2023): 39.

[142]

C. Bedon, “Diagnostic Analysis and Dynamic Identification of a Glass Suspension Footbridge via On-Site Vibration Experiments and FE Numerical Modelling,” Composite Structures 216 (2019): 366-378.

[143]

S. S. Saidin, S. A. Kudus, A. Jamadin, et al., “Vibration-Based Approach for Structural Health Monitoring of Ultra-High-Performance Concrete Bridge,” Case Studies in Construction Materials 18 (2023): e01752.

[144]

Y. Aryan, A. K. Dikshit, and A. M. Shinde, “A Critical Review of the Life Cycle Assessment Studies on Road Pavements and Road Infrastructures,” Journal of Environmental Management 336 (2023): 117697.

[145]

J. Yun, I. Kim, M. Ryoo, Y. Kim, S. Jo, and D. Kim, “Paint Based Triboelectric Nanogenerator Using Facile Spray Deposition Towards Smart Traffic System and Security Application,” Nano Energy 88 (2021): 106236.

[146]

X. Yang, G. Liu, Q. Guo, et al., “Triboelectric Sensor Array for Internet of Things Based Smart Traffic Monitoring and Management System,” Nano Energy 92 (2022): 106757.

[147]

Y. Pang, X. Zhu, Y. Yu, S. Liu, Y. Chen, and Y. Feng, “Waterbomb-Origami Inspired Triboelectric Nanogenerator for Smart Pavement-Integrated Traffic Monitoring,” Nano Research 15 (2022): 5450-5460.

[148]

S. Mishra, M. Rakshita, H. Divi, S. Potu, and R. K. Rajaboina, “Unique Contact Point Modification Technique for Boosting the Performance of a Triboelectric Nanogenerator and Its Application in Road Safety Sensing and Detection,” ACS Applied Materials & Interfaces 15 (2023): 33095-33108.

[149]

H. Zhang, C. Yang, Y. Yu, et al., “Origami-Tessellation-Based Triboelectric Nanogenerator for Energy Harvesting With Application in Road Pavement,” Nano Energy 78 (2020): 105177.

[150]

L. Mingwei and L. Lin, “Intelligent Transportation System in China: The Optimal Evaluation Period of Transportation's Application Performance,” Journal of Intelligent & Fuzzy Systems 38 (2020): 6979-6990.

[151]

J. A. Guerrero-ibanez, S. Zeadally, and J. Contreras-Castillo, “Integration Challenges of Intelligent Transportation Systems With Connected Vehicle, Cloud Computing, and Internet of Things Technologies,” IEEE Wireless Communications 22 (2015): 122-128.

[152]

J. Xiao, B. T. Kulakowski, and M. EI-Gindy, “Prediction of Risk of Wet-Pavement Accidents: Fuzzy Logic Model,” Transportation Research Record 1717 (2000): 28-36.

[153]

B. Mataei, H. Zakeri, M. Zahedi, and F. M. Nejad, “Pavement Friction and Skid Resistance Measurement Methods: A Literature Review,” Journal of Civil Engineering 6, no. 4 (2016): 28.

[154]

Y. Liu, Z. Qian, H. Hu, X. Shi, and L. Chen, “Developing a Skid Resistance Prediction Model for Newly Built Pavement: Application to a Case Study of Steel Bridge Deck Pavement,” Road Materials and Pavement Design 23 (2022): 2334-2352.

[155]

S. Islam and W. G. Buttlar, “Effect of Pavement Roughness on User Costs,” Transportation Research Record 2285 (2012): 47-55.

[156]

Z. Chen, H. Wu, Z. Xia, et al., “A ‘Square Box’-Structured Triboelectric Nanogenerator for Road Transportation Monitoring,” Polymers 14 (2022): 2695.

[157]

Y. Pang, X. Zhu, Y. Jin, et al., “Textile-Inspired Triboelectric Nanogenerator as Intelligent Pavement Energy Harvester and Self-Powered Skid Resistance Sensor,” Applied Energy 348 (2023): 121515.

[158]

K.-X. Hou, X. Dai, S.-P. Zhao, L.-B. Huang, and C.-H. Li, “A Damage-Tolerant, Self-Healing and Multifunctional Triboelectric Nanogenerator,” Nano Energy 116 (2023): 108739.

[159]

Q. Zhang, K. Barri, S. R. Kari, Z. L. Wang, and A. H. Alavi, “Multifunctional Triboelectric Nanogenerator-Enabled Structural Elements for Next Generation Civil Infrastructure Monitoring Systems,” Advanced Functional Materials 31 (2021): 2105825.

[160]

K. Barri, Q. Zhang, J. Kline, et al., “Multifunctional Nanogenerator-Integrated Metamaterial Concrete Systems for Smart Civil Infrastructure,” Advanced Materials 35 (2023): e2211027.

[161]

B. Han and J. Ou, “Embedded Piezoresistive Cement-Based Stress/Strain Sensor,” Sensors and Actuators, A: Physical 138 (2007): 294-298.

[162]

H. Yang and L. Ma, “Multi-Stable Mechanical Metamaterials With Shape-Reconfiguration and Zero Poisson's Ratio,” Materials & Design 152 (2018): 181-190.

[163]

R. Liu, C. Ji, Z. Zhao, and T. Zhou, “Metamaterials: Reshape and Rethink,” Engineering 1 (2015): 179-184.

[164]

M. Binder, V. Mezhuyev, and M. Tschandl, “Predictive Maintenance for Railway Domain: A Systematic Literature Review,” IEEE Engineering Management Review 51 (2023): 120-140.

[165]

H. Li, K. Yu, K. Wang, and A. Zhang, “Market Power and Its Determinants in the Chinese Railway Industry,” Transportation Research Part A: Policy and Practice 120 (2019): 261-276.

[166]

W. Qu, J. Rezaei, Y. Maknoon, and L. Tavasszy, “Hinterland Freight Transportation Replanning Model Under the Framework of Synchromodality,” Transportation Research Part E: Logistics and Transportation Review 131 (2019): 308-328.

[167]

J. Zuo, L. Dong, J. Ding, X. Wang, P. Diao, and J. Yu, “Design and Validation of a Self-Powered Device for Wireless Electronically Controlled Pneumatic Brake and Onboard Monitoring in Freight Wagons,” Energy Conversion and Management 239 (2021): 114229.

[168]

X. Zhao, G. Wei, X. Li, et al., “Self-Powered Triboelectric Nano Vibration Accelerometer Based Wireless Sensor System for Railway State Health Monitoring,” Nano Energy 34 (2017): 549-555.

[169]

Z. Fang, Z. Zhou, M. Yi, Z. Zhang, X. Luo, and A. Ahmed, “A Roller-Bearing-Based Triboelectric Nanosensor for Freight Train Synergistic Maintenance in Smart Transportation,” Nano Energy 106 (2023): 108089.

[170]

Y. Meng, J. Yang, S. Liu, et al., “Nano-Fiber Based Self-Powered Flexible Vibration Sensor for Rail Fasteners Tightness Safety Detection,” Nano Energy 102 (2022): 107667.

[171]

J. Xu, X. Wei, R. Li, S. Kong, Z. Wu, and Z. L. Wang, “A Capsule-Shaped Triboelectric Nanogenerator for Self-Powered Health Monitoring of Traffic Facilities,” ACS Materials Letters 4 (2022): 1630-1637.

[172]

Z. Lin, C. Sun, W. Liu, et al., “A Self-Powered and High-Frequency Vibration Sensor With Layer-Powder-Layer Structure for Structural Health Monitoring,” Nano Energy 90 (2021): 106366.

[173]

C. Zhang, H. Kordestani, and M. Shadabfar, “A Combined Review of Vibration Control Strategies for High-Speed Trains and Railway Infrastructures,” Journal of Low Frequency Noise, Vibration and Active Control 42 (2023): 272-291.

[174]

L. Jin, S. L. Zhang, S. Xu, H. Guo, W. Yang, and Z. L. Wang, “Free-Fixed Rotational Triboelectric Nanogenerator for Self-Powered Real-Time Wheel Monitoring,” Advanced Materials Technologies 6 (2021): 2000918.

[175]

W. Zhang, L. Peng, S. Zheng, X. Guo, and Y. Wang, “Research on the Simulation of Wheelset Response Characteristic Identification of Railway Fastener Loosening,” Mathematical Problems in Engineering 2020 (2020): 1-15.

[176]

H. Fan, Q. Wang, Y. Luo, and B. Li, “Abnormal Railway Fastener Detection Using Minimal Significant Regions and Local Binary Patterns,” Journal of Optical Technology 86 (2019): 799-807.

[177]

C. Wu, P. Wu, J. Wang, R. Jiang, M. Chen, and X. Wang, “Critical Review of Data-Driven Decision-Making in Bridge Operation and Maintenance,” Structure and Infrastructure Engineering 18 (2022): 47-70.

[178]

X. Zhou and X. Zhang, “Thoughts on the Development of Bridge Technology in China,” Engineering 5 (2019): 1120-1130.

[179]

Z. Li, W. Lin, and Y. Zhang, “Real-Time Drive-by Bridge Damage Detection Using Deep Auto-Encoder,” Structures 47 (2023): 1167-1181.

[180]

P. Rizzo and A. Enshaeian, “Challenges in Bridge Health Monitoring: A Review,” Sensors 21 (2021): 4336.

[181]

Y. Fujino and D. M. Siringoringo, “Recent Research and Development Programs for Infrastructures Maintenance, Renovation and Management in Japan,” Structure and Infrastructure Engineering 16 (2020): 3-25.

[182]

E. Figueiredo and J. Brownjohn, “Three Decades of Statistical Pattern Recognition Paradigm for SHM of Bridges,” Structural Health Monitoring 21 (2022): 3018-3054.

[183]

R. Hou and Y. Xia, “Review on the New Development of Vibration-Based Damage Identification for Civil Engineering Structures: 2010-2019,” Journal of Sound and Vibration 491 (2021): 115741.

[184]

S. Sony, S. Gamage, A. Sadhu, and J. Samarabandu, “Vibration-Based Multiclass Damage Detection and Localization Using Long Short-Term Memory Networks,” Structures 35 (2022): 436-451.

[185]

Z. Li, J. Hou, and Ł. Jankowski, “Structural Damage Identification Based on Estimated Additional Virtual Masses and Bayesian Theory,” Structural and Multidisciplinary Optimization 65 (2022): 45.

[186]

D. S. Yang and C. M. Wang, “Bridge Damage Detection Using Reconstructed Mode Shape by Improved Vehicle Scanning Method,” Engineering Structures 263 (2022): 114373.

[187]

R. O. Curadelli, J. D. Riera, D. Ambrosini, and M. G. Amani, “Damage Detection by Means of Structural Damping Identification,” Engineering Structures 30 (2008): 3497-3504.

[188]

J. Shen, Y. Yang, Z. Yang, B. Li, L. Ji, and J. Cheng, “A Multilayer Triboelectric-Electromagnetic Hybrid Nanogenerator for Vibration Energy Harvesting and Frequency Monitoring,” Nano Energy 116 (2023): 108818.

[189]

L. Liang, X. Wang, M. Li, et al., “Self-Powered Active Vibration Sensor by Peak-Valley Data Processing Independent of the Environment Toward Structural Health Monitoring,” Nano Energy 117 (2023): 108935.

[190]

H. Zhang, K. Huang, Y. Zhou, L. Sun, Z. Zhang, and J. Luo, “A Real-Time Sensing System Based on Triboelectric Nanogenerator for Dynamic Response of Bridges,” Science China: Technological Sciences 65 (2022): 2723-2733.

[191]

K. Huang, Y. Zhou, Z. Zhang, et al., “A Real-Time Quantitative Acceleration Monitoring Method Based on Triboelectric Nanogenerator for Bridge Cable Vibration,” Nano Energy 118 (2023): 108960.

[192]

X. Han, Q. Zhang, J. Yu, et al., “Self-Powered Acceleration Sensor Based on Multilayer Suspension Structure and TPU-RTV Film for Vibration Monitoring,” Nanomaterials 11 (2021): 2763.

[193]

Y. K. Pang, X. H. Li, M. X. Chen, C. B. Han, C. Zhang, and Z. L. Wang, “Triboelectric Nanogenerators as a Self-Powered 3D Acceleration Sensor,” ACS Applied Materials & Interfaces 7 (2015): 19076-19082.

[194]

Z. Zhao, X. Pu, C. Du, et al., “Freestanding Flag-Type Triboelectric Nanogenerator for Harvesting High-Altitude Wind Energy From Arbitrary Directions,” ACS Nano 10 (2016): 1780-1787.

[195]

B. Cheng, Q. Xu, Y. Ding, et al., “High Performance Temperature Difference Triboelectric Nanogenerator,” Nature Communications 12 (2021): 4782.

[196]

L. Liu, Z. Zhao, Y. Li, et al., “Achieving Ultrahigh Effective Surface Charge Density of Direct-Current Triboelectric Nanogenerator in High Humidity,” Small 18 (2022): 2201402.

[197]

Y.-Q. Ni and Y.-X. Xia, “Strain-Based Condition Assessment of a Suspension Bridge Instrumented With Structural Health Monitoring System,” International Journal of Structural Stability and Dynamics 16 (2016): 1640027.

[198]

Y. Q. Ni, X. W. Ye, and J. M. Ko, “Monitoring-Based Fatigue Reliability Assessment of Steel Bridges: Analytical Model and Application,” Journal of Structural Engineering 136 (2010): 1563-1573.

[199]

I. Farreras-Alcover, M. K. Chryssanthopoulos, and J. E. Andersen, “Data-Based Models for Fatigue Reliability of Orthotropic Steel Bridge Decks Based on Temperature, Traffic and Strain Monitoring,” International Journal of Fatigue 95 (2017): 104-119.

[200]

H. Zhang, L. Yao, L. Quan, and X. Zheng, “Theories for Triboelectric Nanogenerators: A Comprehensive Review,” Nanotechnology Reviews 9 (2020): 610-625.

[201]

H. Zhang, C. Zhang, J. Zhang, et al., “A Theoretical Approach for Optimizing Sliding-Mode Triboelectric Nanogenerator Based on Multi-Parameter Analysis,” Nano Energy 61 (2019): 442-453.

[202]

H. Zhang, L. Quan, J. Chen, et al., “A General Optimization Approach for Contact-Separation Triboelectric Nanogenerator,” Nano Energy 56 (2019): 700-707.

[203]

Y. Mohammadi and K. Ahmadi, “Single Degree-of-Freedom Modeling of the Nonlinear Vibration Response of a Machining Robot,” Journal of Manufacturing Science and Engineering 143 (2020): 051003.

[204]

B. Tang and M. J. Brennan, “A Comparison of the Effects of Nonlinear Damping on the Free Vibration of a Single-Degree-of-Freedom System,” Journal of Vibration and Acoustics 134 (2012): 024501.

[205]

Y. Jung, J. Yu, H. J. Hwang, D. Bhatia, K.-B. Chung, and D. Choi, “Wire-Based Triboelectric Resonator for a Self-Powered Crack Monitoring System,” Nano Energy 71 (2020): 104615.

[206]

G. P. Harrison, E. J. Maclean, S. Karamanlis, and L. F. Ochoa, “Life Cycle Assessment of the Transmission Network in Great Britain,” Energy Policy 38 (2010): 3622-3631.

[207]

R. R. Mohassel, A. Fung, F. Mohammadi, and K. Raahemifar, “A Survey on Advanced Metering Infrastructure,” International Journal of Electrical Power & Energy Systems 63 (2014): 473-484.

[208]

J. Luo, X. Shi, P. Chen, et al., “Strong and Flame-Retardant Wood-Based Triboelectric Nanogenerators Toward Self-Powered Building Fire Protection,” Materials Today Physics 27 (2022): 100798.

[209]

L. Zhu, Z. Zhang, D. Kong, et al., “A Triboelectric Nanogenerator Sensor Based on Phononic Crystal Structures for Smart Buildings and Transportation Systems,” Nano Energy 97 (2022): 107165.

[210]

C. He, T. Yang, J. Fang, et al., “Tensegrity-Inspired Triboelectric Nanogenerator for Broadband and Impact-Resistive Vibration Sensing,” Nano Energy 109 (2023): 108279.

[211]

L. Fang, Q. Zheng, W. Hou, J. Gu, and L. Zheng, “A Self-Powered Tilt Angle Sensor for Tall Buildings Based on the Coupling of Multiple Triboelectric Nanogenerator Units,” Sensors and Actuators, A: Physical 349 (2023): 114015.

[212]

S. Hu, Z. Yuan, R. Li, et al., “Vibration-Driven Triboelectric Nanogenerator for Vibration Attenuation and Condition Monitoring for Transmission Lines,” Nano Letters 22 (2022): 5584-5591.

[213]

P. De Frenne, J. Lenoir, M. Luoto, et al., “Forest Microclimates and Climate Change: Importance, Drivers and Future Research Agenda,” Global Change Biology 27 (2021): 2279-2297.

[214]

R. Heilmayr, C. Echeverría, and E. F. Lambin, “Impacts of Chilean Forest Subsidies on Forest Cover, Carbon and Biodiversity,” Nature Sustainability 3 (2020): 701-709.

[215]

S. C. Cook-Patton, S. M. Leavitt, D. Gibbs, et al., “Mapping Carbon Accumulation Potential From Global Natural Forest Regrowth,” Nature 585 (2020): 545-550.

[216]

M. M. Boer, V. Resco de Dios, and R. A. Bradstock, “Unprecedented Burn Area of Australian Mega Forest Fires,” Nature Climate Change 10 (2020): 171-172.

[217]

P. Barmpoutis, P. Papaioannou, K. Dimitropoulos, and N. Grammalidis, "A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing," Sensors 20 (2020): 6442.

[218]

R. Xu, H. Lin, K. Lu, L. Cao, and Y. Liu, "A Forest Fire Detection System Based on Ensemble Learning," Forests 12 (2021): 217.

[219]

R. Cheng, K. Dong, L. Liu, et al., “Flame-Retardant Textile-Based Triboelectric Nanogenerators for Fire Protection Applications,” ACS Nano 14 (2020): 15853-15863.

[220]

S. Fu, W. He, Q. Tang, et al., “An Ultrarobust and High-Performance Rotational Hydrodynamic Triboelectric Nanogenerator Enabled by Automatic Mode Switching and Charge Excitation,” Advanced Materials 34 (2022): 2105882.

[221]

J. Doshi, T. Patel, and S. Bharti, “Smart Farming Using IoT, a Solution for Optimally Monitoring Farming Conditions,” Procedia Computer Science 160 (2019): 746-751.

[222]

A. Rajput and V. B. Kumaravelu, “Fuzzy Logic-Based Distributed Clustering Protocol to Improve Energy Efficiency and Stability of Wireless Smart Sensor Networks for Farmland Monitoring Systems,” International Journal of Communication Systems 33 (2020): e4239.

[223]

L. F. Si, M. Li, and L. He, “Farmland Monitoring and Livestock Management Based on Internet of Things,” Internet of Things 19 (2022): 100581.

[224]

A. Sharma, M. Georgi, M. Tregubenko, A. Tselykh, and A. Tselykh, “Enabling Smart Agriculture by Implementing Artificial Intelligence and Embedded Sensing,” Computers & Industrial Engineering 165 (2022): 107936.

[225]

P. K. Reddy Maddikunta, S. Hakak, M. Alazab, et al., “Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements, and Challenges,” IEEE Sensors Journal 21 (2021): 17608-17619.

[226]

R. Rayhana, G. Xiao, and Z. Liu, “RFID Sensing Technologies for Smart Agriculture,” IEEE Instrumentation & Measurement Magazine 24 (2021): 50-60.

[227]

G. Gu, G. Gu, W. Shang, et al., “The Self-Powered Agricultural Sensing System With 1.7 km Wireless Multichannel Signal Transmission Using a Pulsed Triboelectric Nanogenerator of Corn Husk Composite Film,” Nano Energy 102 (2022): 107699.

[228]

X. Li, Y. Cao, X. Yu, et al., “Breeze-Driven Triboelectric Nanogenerator for Wind Energy Harvesting and Application in Smart Agriculture,” Applied Energy 306 (2022): 117977.

[229]

X. Li, J. Luo, K. Han, et al., “Stimulation of Ambient Energy Generated Electric Field on Crop Plant Growth,” Nature Food 3 (2022): 133-142.

[230]

S. Gao, R. Wang, S. Feng, et al., “Self-Powered System by an Aerodynamic-Complementary Triboelectric-Electromagnetic Hybridized Generator With Triple-Mode Switching Power Management Topology for Wide-Range Wind Energy Collection and Climate Monitoring,” Advanced Materials Technologies 10 (2025): 2401840.

[231]

N. Hanikel, X. Pei, S. Chheda, et al., “Evolution of Water Structures in Metal-Organic Frameworks for Improved Atmospheric Water Harvesting,” Science 374 (2021): 454-459.

[232]

B. R. Scanlon, S. Fakhreddine, A. Rateb, et al., “Global Water Resources and the Role of Groundwater in a Resilient Water Future,” Nature Reviews Earth & Environment 4 (2023): 87-101.

[233]

Z. Zheng, H. L. Nguyen, N. Hanikel, et al., “High-Yield, Green and Scalable Methods for Producing MOF-303 for Water Harvesting From Desert Air,” Nature Protocols 18 (2023): 136-156.

[234]

G. Englander, “Property Rights and the Protection of Global Marine Resources,” Nature Sustainability 2 (2019): 981-987.

[235]

M. Garcia, E. Koebele, A. Deslatte, K. Ernst, K. F. Manago, and G. Treuer, “Towards Urban Water Sustainability: Analyzing Management Transitions in Miami, Las Vegas, and Los Angeles,” Global Environmental Change 58 (2019): 101967.

[236]

Y. Guo, J. Bae, Z. Fang, P. Li, F. Zhao, and G. Yu, “Hydrogels and Hydrogel-Derived Materials for Energy and Water Sustainability,” Chemical Reviews 120 (2020): 7642-7707.

[237]

J. Silva, V. Fernandes, M. Limont, M. Dziedzic, C. V. Andreoli, and W. B. Rauen, “Water Sustainability Assessment From the Perspective of Sustainable Development Capitals: Conceptual Model and Index Based on Literature Review,” Journal of Environmental Management 254 (2020): 109750.

[238]

C. Zhu, C. Xiang, M. Wu, et al., “Recent Advances in Wave-Driven Triboelectric Nanogenerators: From Manufacturing to Applications,” International Journal of Extreme Manufacturing 6 (2024): 062009.

[239]

J. Liu, P. Xu, B. Liu, et al., “Underwater Biomimetic Lateral Line Sensor Based on Triboelectric Nanogenerator for Dynamic Pressure Monitoring and Trajectory Perception,” Small 20 (2024): 2308491.

[240]

J. Tollefson, “Power From the Oceans: Blue Energy,” Nature 508 (2014): 302-304.

[241]

Y. Xu, W. Yang, X. Lu, et al., “Triboelectric Nanogenerator for Ocean Wave Graded Energy Harvesting and Condition Monitoring,” ACS Nano 15 (2021): 16368-16375.

[242]

C. Zhang, Y. Hao, J. Yang, et al., “Recent Advances in Triboelectric Nanogenerators for Marine Exploitation,” Advanced Energy Materials 13 (2023): 2300387.

[243]

R. Derie, “Formaldehyde Oxime Leaching of Metals From Deep-Sea Manganese Nodules and Other Ores,” Nature 324 (1986): 660-661.

[244]

X. Zhang, M. Yu, Z. Ma, et al., “Self-Powered Distributed Water Level Sensors Based on Liquid-Solid Triboelectric Nanogenerators for Ship Draft Detecting,” Advanced Functional Materials 29 (2019): 1900327.

[245]

C. Zhang, L. Liu, L. Zhou, et al., “Self-Powered Sensor for Quantifying Ocean Surface Water Waves Based on Triboelectric Nanogenerator,” ACS Nano 14 (2020): 7092-7100.

[246]

M. Xu, S. Wang, S. L. Zhang, et al., “A Highly-Sensitive Wave Sensor Based on Liquid-Solid Interfacing Triboelectric Nanogenerator for Smart Marine Equipment,” Nano Energy 57 (2019): 574-580.

[247]

W. Zhang, W. Bao, X. , and D. Diao, “Friction Force Excitation Effect on the Sliding-Mode Triboelectric Nanogenerator,” Tribology International 185 (2023): 108504.

[248]

S. Fu and C. Hu, “Achieving Ultra-Durability and High Output Performance of Triboelectric Nanogenerators,” Advanced Functional Materials 34 (2023): 2308138.

[249]

Y. Dong, N. Wang, D. Yang, J. Wang, W. Lu, and D. Wang, “Robust Solid-Liquid Triboelectric Nanogenerators: Mechanisms, Strategies and Applications,” Advanced Functional Materials 33 (2023): 2300764.

[250]

N. Tritschler, A. Dugenske, and T. Kurfess, “An Automated Edge Computing-Based Condition Health Monitoring System: With an Application on Rolling Element Bearings,” Journal of Manufacturing Science and Engineering 143 (2021): 071006.

[251]

F. Dong, M. Zhu, Y. Wang, et al., “AI-Enabled Rolling Triboelectric Nanogenerator for Bearing Wear Diagnosis Aiming at Digital Twin Application,” Nano Energy 134 (2025): 110550.

RIGHTS & PERMISSIONS

2025 The Author(s). Interdisciplinary Materials published by Wuhan University of Technology and John Wiley & Sons Australia, Ltd.

AI Summary AI Mindmap
PDF

0

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/