Compound Motion‐Mode Tribovoltaic Nanogenerator for Self‐Powered Monitoring of Gear Transmission System
Source abstract
Abstract
Abstract Tribovoltaic nanogenerators (TVNGs) are an advanced technology that can convert friction energy into electrical energy via semiconductor tribovoltaic effect, which offers the advantages of high charge density and direct current (DC) output. However, characteristic components of TVNG outputs are relatively few, which is not conducive to monitoring the periodic motions. This study proposes a compound motion‐mode tribovoltaic nanogenerator (CM‐TVNG) for monitoring the gear transmission system using gears' unique meshing transmission form. The CM‐TVNG uses the approach‐slide‐separation composite motion of silicon wafers attached to both sides of the tooth slots and the tooth top of the mating gear to generate a DC signal with discernible periodic characteristics. The working principle of CM‐TVNG is verified by theoretical analysis and COMSOL dynamic simulation. The main working parameters verify CM‐TVNG output characteristics and applicability under various conditions. Subsequently, CM‐TVNG's ability to detect gear's state is verified. Using a deep learning (DL) model, gear faults are diagnosed with a diagnostic accuracy of up to 95.5%. The feasibility of CM‐TVNG is demonstrated by application in an industrial‐grade parallel‐gear transmission system. In particular, speed monitoring capability under variable speeds is verified, demonstrating that it can be applied to monitor the stability of gear transmission systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Force Microscopy Techniques and Applications · Adhesion, Friction, and Surface Interactions · Lubricants and Their Additives
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Tribology Department of Mechanical Engineering Tsinghua University Beijing 100084 China
Beijing Key Laboratory of Micro‐nano Energy and Sensor Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing 101400 China
State Key Laboratory of Mechanical Transmission College of Mechanical Engineering Chongqing University Chongqing 400044 China
Funding
National Natural Science Foundation of China · State Key Laboratory of Tribology
References (36)
Show source-provided references
1. 10.1016/j.mejo.2008.08.01210.1016/j.mejo.2008.08.012 ↗
2. 10.1109/SURV.2014.012114.0005810.1109/SURV.2014.012114.00058 ↗
3. 10.1016/j.sintl.2021.10011010.1016/j.sintl.2021.100110 ↗
4. 10.3390/s2206208710.3390/s22062087 ↗
5. 10.1109/TIM.2021.311809210.1109/TIM.2021.3118092 ↗
6. Zhao Y. · Prot. Control Mod. Power Syst. · 2024 · 9 · 96
7. 10.1007/s40857-021-00217-610.1007/s40857-021-00217-6 ↗
8. 10.3390/jmse1112236310.3390/jmse11122363 ↗
9. 10.1007/s40544-017-0183-510.1007/s40544-017-0183-5 ↗
10. 10.1016/j.nanoen.2022.10738710.1016/j.nanoen.2022.107387 ↗
11. 10.1016/j.nanoen.2022.10775910.1016/j.nanoen.2022.107759 ↗
12. 10.1016/j.nanoen.2024.10965510.1016/j.nanoen.2024.109655 ↗
13. 10.1016/j.nanoen.2024.10942910.1016/j.nanoen.2024.109429 ↗
14. 10.1016/j.sna.2023.11445510.1016/j.sna.2023.114455 ↗
15. 10.1038/s41467-021-25047-y10.1038/s41467-021-25047-y ↗
16. 10.1002/aenm.20200062710.1002/aenm.202000627 ↗
17. 10.1002/adma.20180439810.1002/adma.201804398 ↗
18. 10.1016/j.matt.2022.02.02110.1016/j.matt.2022.02.021 ↗
19. 10.1038/s41565-017-0019-510.1038/s41565-017-0019-5 ↗
20. 10.1002/aenm.20190371310.1002/aenm.201903713 ↗
21. 10.1039/D2EE04019K10.1039/D2EE04019K ↗
22. 10.1016/j.mattod.2022.11.00510.1016/j.mattod.2022.11.005 ↗
23. 10.1016/j.mtnano.2023.10031810.1016/j.mtnano.2023.100318 ↗
24. 10.1016/j.ceramint.2024.04.11310.1016/j.ceramint.2024.04.113 ↗
25. 10.1002/aenm.20230007910.1002/aenm.202300079 ↗
26. 10.1002/adma.20231009810.1002/adma.202310098 ↗
27. 10.1002/adfm.20230510610.1002/adfm.202305106 ↗
28. 10.1016/j.nanoen.2022.10785110.1016/j.nanoen.2022.107851 ↗
29. 10.1016/j.nanoen.2021.10674710.1016/j.nanoen.2021.106747 ↗
30. 10.1016/j.nanoen.2022.10808910.1016/j.nanoen.2022.108089 ↗
31. 10.1109/TMECH.2022.316402210.1109/TMECH.2022.3164022 ↗
32. 10.1016/j.nanoen.2022.10726310.1016/j.nanoen.2022.107263 ↗
33. 10.3390/app918364210.3390/app9183642 ↗
34. 10.1016/j.ymssp.2019.02.05610.1016/j.ymssp.2019.02.056 ↗
35. S.Targ D.Almeida K.Lyman Resnet in Resnet: Generalizing Residual Architectures http://arxiv.org/abs/1603.08029 (accessed: 2016).
36. 10.1109/TII.2019.294186810.1109/TII.2019.2941868 ↗