A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network

Summary: The electric signals of cantilever energy harvesting devices with/without a crack were mainly obtained by external sensors, so detecting device damage on a large scale is difficult. To tackle the issue, a cantilever-structure freestanding triboelectric nanogenerator (CSF-TENG) device was pr...
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Autor*in:	Chuanfu Xin [verfasserIn] Zifeng Xu [verfasserIn] Ying Gong [verfasserIn] Hengyu Guo [verfasserIn] Zhongjie Li [verfasserIn] Jiheng Ding [verfasserIn] Shaorong Xie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Materials science Materials chemistry Energy materials

Übergeordnetes Werk:	In: iScience - Elsevier, 2019, 25(2022), 12, Seite 105673-
Übergeordnetes Werk:	volume:25 ; year:2022 ; number:12 ; pages:105673-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.isci.2022.105673

Katalog-ID:	DOAJ025207946

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520			\|a Summary: The electric signals of cantilever energy harvesting devices with/without a crack were mainly obtained by external sensors, so detecting device damage on a large scale is difficult. To tackle the issue, a cantilever-structure freestanding triboelectric nanogenerator (CSF-TENG) device was proposed, which can scavenge ambient energy and act as a self-powered sensor. Firstly, the relation between the peak-to-peak voltage and amplitude of the CSF-TENG was established. Next, the output performance of the CSF-TENG was measured. Then, depending on electric signals output by the CSF-TENG, a cantilever defect identification model was built by using the wavelet packet and long short-term memory (LSTM) algorithms. The experimental results manifest that the accuracy of the model is about 98.6%. Thus, the CSF-TENG with a crack can be detected timely due to its self-monitoring ability, which is of great significance for the development of self-powered sensor networks.
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allfields	10.1016/j.isci.2022.105673 doi (DE-627)DOAJ025207946 (DE-599)DOAJcc7e65fb0c9e4e768424d3cac466551f DE-627 ger DE-627 rakwb eng Chuanfu Xin verfasserin aut A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: The electric signals of cantilever energy harvesting devices with/without a crack were mainly obtained by external sensors, so detecting device damage on a large scale is difficult. To tackle the issue, a cantilever-structure freestanding triboelectric nanogenerator (CSF-TENG) device was proposed, which can scavenge ambient energy and act as a self-powered sensor. Firstly, the relation between the peak-to-peak voltage and amplitude of the CSF-TENG was established. Next, the output performance of the CSF-TENG was measured. Then, depending on electric signals output by the CSF-TENG, a cantilever defect identification model was built by using the wavelet packet and long short-term memory (LSTM) algorithms. The experimental results manifest that the accuracy of the model is about 98.6%. Thus, the CSF-TENG with a crack can be detected timely due to its self-monitoring ability, which is of great significance for the development of self-powered sensor networks. Materials science Materials chemistry Energy materials Science Q Zifeng Xu verfasserin aut Ying Gong verfasserin aut Hengyu Guo verfasserin aut Zhongjie Li verfasserin aut Jiheng Ding verfasserin aut Shaorong Xie verfasserin aut In iScience Elsevier, 2019 25(2022), 12, Seite 105673- (DE-627)1019532106 25890042 nnns volume:25 year:2022 number:12 pages:105673- https://doi.org/10.1016/j.isci.2022.105673 kostenfrei https://doaj.org/article/cc7e65fb0c9e4e768424d3cac466551f kostenfrei http://www.sciencedirect.com/science/article/pii/S2589004222019459 kostenfrei https://doaj.org/toc/2589-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 25 2022 12 105673-
spelling	10.1016/j.isci.2022.105673 doi (DE-627)DOAJ025207946 (DE-599)DOAJcc7e65fb0c9e4e768424d3cac466551f DE-627 ger DE-627 rakwb eng Chuanfu Xin verfasserin aut A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: The electric signals of cantilever energy harvesting devices with/without a crack were mainly obtained by external sensors, so detecting device damage on a large scale is difficult. To tackle the issue, a cantilever-structure freestanding triboelectric nanogenerator (CSF-TENG) device was proposed, which can scavenge ambient energy and act as a self-powered sensor. Firstly, the relation between the peak-to-peak voltage and amplitude of the CSF-TENG was established. Next, the output performance of the CSF-TENG was measured. Then, depending on electric signals output by the CSF-TENG, a cantilever defect identification model was built by using the wavelet packet and long short-term memory (LSTM) algorithms. The experimental results manifest that the accuracy of the model is about 98.6%. Thus, the CSF-TENG with a crack can be detected timely due to its self-monitoring ability, which is of great significance for the development of self-powered sensor networks. Materials science Materials chemistry Energy materials Science Q Zifeng Xu verfasserin aut Ying Gong verfasserin aut Hengyu Guo verfasserin aut Zhongjie Li verfasserin aut Jiheng Ding verfasserin aut Shaorong Xie verfasserin aut In iScience Elsevier, 2019 25(2022), 12, Seite 105673- (DE-627)1019532106 25890042 nnns volume:25 year:2022 number:12 pages:105673- https://doi.org/10.1016/j.isci.2022.105673 kostenfrei https://doaj.org/article/cc7e65fb0c9e4e768424d3cac466551f kostenfrei http://www.sciencedirect.com/science/article/pii/S2589004222019459 kostenfrei https://doaj.org/toc/2589-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 25 2022 12 105673-
allfields_unstemmed	10.1016/j.isci.2022.105673 doi (DE-627)DOAJ025207946 (DE-599)DOAJcc7e65fb0c9e4e768424d3cac466551f DE-627 ger DE-627 rakwb eng Chuanfu Xin verfasserin aut A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: The electric signals of cantilever energy harvesting devices with/without a crack were mainly obtained by external sensors, so detecting device damage on a large scale is difficult. To tackle the issue, a cantilever-structure freestanding triboelectric nanogenerator (CSF-TENG) device was proposed, which can scavenge ambient energy and act as a self-powered sensor. Firstly, the relation between the peak-to-peak voltage and amplitude of the CSF-TENG was established. Next, the output performance of the CSF-TENG was measured. Then, depending on electric signals output by the CSF-TENG, a cantilever defect identification model was built by using the wavelet packet and long short-term memory (LSTM) algorithms. The experimental results manifest that the accuracy of the model is about 98.6%. Thus, the CSF-TENG with a crack can be detected timely due to its self-monitoring ability, which is of great significance for the development of self-powered sensor networks. Materials science Materials chemistry Energy materials Science Q Zifeng Xu verfasserin aut Ying Gong verfasserin aut Hengyu Guo verfasserin aut Zhongjie Li verfasserin aut Jiheng Ding verfasserin aut Shaorong Xie verfasserin aut In iScience Elsevier, 2019 25(2022), 12, Seite 105673- (DE-627)1019532106 25890042 nnns volume:25 year:2022 number:12 pages:105673- https://doi.org/10.1016/j.isci.2022.105673 kostenfrei https://doaj.org/article/cc7e65fb0c9e4e768424d3cac466551f kostenfrei http://www.sciencedirect.com/science/article/pii/S2589004222019459 kostenfrei https://doaj.org/toc/2589-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 25 2022 12 105673-
allfieldsGer	10.1016/j.isci.2022.105673 doi (DE-627)DOAJ025207946 (DE-599)DOAJcc7e65fb0c9e4e768424d3cac466551f DE-627 ger DE-627 rakwb eng Chuanfu Xin verfasserin aut A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: The electric signals of cantilever energy harvesting devices with/without a crack were mainly obtained by external sensors, so detecting device damage on a large scale is difficult. To tackle the issue, a cantilever-structure freestanding triboelectric nanogenerator (CSF-TENG) device was proposed, which can scavenge ambient energy and act as a self-powered sensor. Firstly, the relation between the peak-to-peak voltage and amplitude of the CSF-TENG was established. Next, the output performance of the CSF-TENG was measured. Then, depending on electric signals output by the CSF-TENG, a cantilever defect identification model was built by using the wavelet packet and long short-term memory (LSTM) algorithms. The experimental results manifest that the accuracy of the model is about 98.6%. Thus, the CSF-TENG with a crack can be detected timely due to its self-monitoring ability, which is of great significance for the development of self-powered sensor networks. Materials science Materials chemistry Energy materials Science Q Zifeng Xu verfasserin aut Ying Gong verfasserin aut Hengyu Guo verfasserin aut Zhongjie Li verfasserin aut Jiheng Ding verfasserin aut Shaorong Xie verfasserin aut In iScience Elsevier, 2019 25(2022), 12, Seite 105673- (DE-627)1019532106 25890042 nnns volume:25 year:2022 number:12 pages:105673- https://doi.org/10.1016/j.isci.2022.105673 kostenfrei https://doaj.org/article/cc7e65fb0c9e4e768424d3cac466551f kostenfrei http://www.sciencedirect.com/science/article/pii/S2589004222019459 kostenfrei https://doaj.org/toc/2589-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 25 2022 12 105673-
allfieldsSound	10.1016/j.isci.2022.105673 doi (DE-627)DOAJ025207946 (DE-599)DOAJcc7e65fb0c9e4e768424d3cac466551f DE-627 ger DE-627 rakwb eng Chuanfu Xin verfasserin aut A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summary: The electric signals of cantilever energy harvesting devices with/without a crack were mainly obtained by external sensors, so detecting device damage on a large scale is difficult. To tackle the issue, a cantilever-structure freestanding triboelectric nanogenerator (CSF-TENG) device was proposed, which can scavenge ambient energy and act as a self-powered sensor. Firstly, the relation between the peak-to-peak voltage and amplitude of the CSF-TENG was established. Next, the output performance of the CSF-TENG was measured. Then, depending on electric signals output by the CSF-TENG, a cantilever defect identification model was built by using the wavelet packet and long short-term memory (LSTM) algorithms. The experimental results manifest that the accuracy of the model is about 98.6%. Thus, the CSF-TENG with a crack can be detected timely due to its self-monitoring ability, which is of great significance for the development of self-powered sensor networks. Materials science Materials chemistry Energy materials Science Q Zifeng Xu verfasserin aut Ying Gong verfasserin aut Hengyu Guo verfasserin aut Zhongjie Li verfasserin aut Jiheng Ding verfasserin aut Shaorong Xie verfasserin aut In iScience Elsevier, 2019 25(2022), 12, Seite 105673- (DE-627)1019532106 25890042 nnns volume:25 year:2022 number:12 pages:105673- https://doi.org/10.1016/j.isci.2022.105673 kostenfrei https://doaj.org/article/cc7e65fb0c9e4e768424d3cac466551f kostenfrei http://www.sciencedirect.com/science/article/pii/S2589004222019459 kostenfrei https://doaj.org/toc/2589-0042 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 25 2022 12 105673-
language	English
source	In iScience 25(2022), 12, Seite 105673- volume:25 year:2022 number:12 pages:105673-
sourceStr	In iScience 25(2022), 12, Seite 105673- volume:25 year:2022 number:12 pages:105673-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Materials science Materials chemistry Energy materials Science Q
isfreeaccess_bool	true
container_title	iScience
authorswithroles_txt_mv	Chuanfu Xin @@aut@@ Zifeng Xu @@aut@@ Ying Gong @@aut@@ Hengyu Guo @@aut@@ Zhongjie Li @@aut@@ Jiheng Ding @@aut@@ Shaorong Xie @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	1019532106
id	DOAJ025207946
language_de	englisch
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author	Chuanfu Xin
spellingShingle	Chuanfu Xin misc Materials science misc Materials chemistry misc Energy materials misc Science misc Q A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network
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topic_title	A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network Materials science Materials chemistry Energy materials
topic	misc Materials science misc Materials chemistry misc Energy materials misc Science misc Q
topic_unstemmed	misc Materials science misc Materials chemistry misc Energy materials misc Science misc Q
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title	A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network
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title_full	A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network
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author_browse	Chuanfu Xin Zifeng Xu Ying Gong Hengyu Guo Zhongjie Li Jiheng Ding Shaorong Xie
container_volume	25
format_se	Elektronische Aufsätze
author-letter	Chuanfu Xin
doi_str_mv	10.1016/j.isci.2022.105673
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title_sort	cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network
title_auth	A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network
abstract	Summary: The electric signals of cantilever energy harvesting devices with/without a crack were mainly obtained by external sensors, so detecting device damage on a large scale is difficult. To tackle the issue, a cantilever-structure freestanding triboelectric nanogenerator (CSF-TENG) device was proposed, which can scavenge ambient energy and act as a self-powered sensor. Firstly, the relation between the peak-to-peak voltage and amplitude of the CSF-TENG was established. Next, the output performance of the CSF-TENG was measured. Then, depending on electric signals output by the CSF-TENG, a cantilever defect identification model was built by using the wavelet packet and long short-term memory (LSTM) algorithms. The experimental results manifest that the accuracy of the model is about 98.6%. Thus, the CSF-TENG with a crack can be detected timely due to its self-monitoring ability, which is of great significance for the development of self-powered sensor networks.
abstractGer	Summary: The electric signals of cantilever energy harvesting devices with/without a crack were mainly obtained by external sensors, so detecting device damage on a large scale is difficult. To tackle the issue, a cantilever-structure freestanding triboelectric nanogenerator (CSF-TENG) device was proposed, which can scavenge ambient energy and act as a self-powered sensor. Firstly, the relation between the peak-to-peak voltage and amplitude of the CSF-TENG was established. Next, the output performance of the CSF-TENG was measured. Then, depending on electric signals output by the CSF-TENG, a cantilever defect identification model was built by using the wavelet packet and long short-term memory (LSTM) algorithms. The experimental results manifest that the accuracy of the model is about 98.6%. Thus, the CSF-TENG with a crack can be detected timely due to its self-monitoring ability, which is of great significance for the development of self-powered sensor networks.
abstract_unstemmed	Summary: The electric signals of cantilever energy harvesting devices with/without a crack were mainly obtained by external sensors, so detecting device damage on a large scale is difficult. To tackle the issue, a cantilever-structure freestanding triboelectric nanogenerator (CSF-TENG) device was proposed, which can scavenge ambient energy and act as a self-powered sensor. Firstly, the relation between the peak-to-peak voltage and amplitude of the CSF-TENG was established. Next, the output performance of the CSF-TENG was measured. Then, depending on electric signals output by the CSF-TENG, a cantilever defect identification model was built by using the wavelet packet and long short-term memory (LSTM) algorithms. The experimental results manifest that the accuracy of the model is about 98.6%. Thus, the CSF-TENG with a crack can be detected timely due to its self-monitoring ability, which is of great significance for the development of self-powered sensor networks.
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title_short	A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network
url	https://doi.org/10.1016/j.isci.2022.105673 https://doaj.org/article/cc7e65fb0c9e4e768424d3cac466551f http://www.sciencedirect.com/science/article/pii/S2589004222019459 https://doaj.org/toc/2589-0042
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up_date	2024-07-03T13:38:30.893Z
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A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network

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