Multispectral radiometric temperature measurement algorithm for turbine blades based on moving narrow-band spectral windows
This paper addresses the problem of inaccurate emissivity presets for multispectral temperature measurements of aero-engine turbine blades and proposes a narrow-band spectral window moving temperature inversion algorithm that does not rely on an assumed emissivity model. As the emissivity of the mea...
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| Veröffentlicht in: | Optics express Jg. 29; H. 3; S. 4405 |
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01.02.2021
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| ISSN: | 1094-4087, 1094-4087 |
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| Abstract | This paper addresses the problem of inaccurate emissivity presets for multispectral temperature measurements of aero-engine turbine blades and proposes a narrow-band spectral window moving temperature inversion algorithm that does not rely on an assumed emissivity model. As the emissivity of the measured object changes slowly over the narrow spectral window, the temperature corresponding to the normalized spectral radiation intensity for each window in the set temperature range is calculated using the Mahalanobis distance coefficient. The temperature error is less than 1.33% relative to thermocouple measurements when using this algorithm to perform temperature inversion on the experimental spectrum curves for different types of alloy samples. Furthermore, a two-dimensional spectral temperature field measurement platform was built, and the surface temperature fields of alloy samples were reconstructed using the narrow-band spectral window moving algorithm. The proposed algorithm is shown to provide high-precision inversion of the temperature field without presetting the emissivity model, which gives a new processing concept for the application of infrared spectral temperature measurements. |
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| AbstractList | This paper addresses the problem of inaccurate emissivity presets for multispectral temperature measurements of aero-engine turbine blades and proposes a narrow-band spectral window moving temperature inversion algorithm that does not rely on an assumed emissivity model. As the emissivity of the measured object changes slowly over the narrow spectral window, the temperature corresponding to the normalized spectral radiation intensity for each window in the set temperature range is calculated using the Mahalanobis distance coefficient. The temperature error is less than 1.33% relative to thermocouple measurements when using this algorithm to perform temperature inversion on the experimental spectrum curves for different types of alloy samples. Furthermore, a two-dimensional spectral temperature field measurement platform was built, and the surface temperature fields of alloy samples were reconstructed using the narrow-band spectral window moving algorithm. The proposed algorithm is shown to provide high-precision inversion of the temperature field without presetting the emissivity model, which gives a new processing concept for the application of infrared spectral temperature measurements.This paper addresses the problem of inaccurate emissivity presets for multispectral temperature measurements of aero-engine turbine blades and proposes a narrow-band spectral window moving temperature inversion algorithm that does not rely on an assumed emissivity model. As the emissivity of the measured object changes slowly over the narrow spectral window, the temperature corresponding to the normalized spectral radiation intensity for each window in the set temperature range is calculated using the Mahalanobis distance coefficient. The temperature error is less than 1.33% relative to thermocouple measurements when using this algorithm to perform temperature inversion on the experimental spectrum curves for different types of alloy samples. Furthermore, a two-dimensional spectral temperature field measurement platform was built, and the surface temperature fields of alloy samples were reconstructed using the narrow-band spectral window moving algorithm. The proposed algorithm is shown to provide high-precision inversion of the temperature field without presetting the emissivity model, which gives a new processing concept for the application of infrared spectral temperature measurements. This paper addresses the problem of inaccurate emissivity presets for multispectral temperature measurements of aero-engine turbine blades and proposes a narrow-band spectral window moving temperature inversion algorithm that does not rely on an assumed emissivity model. As the emissivity of the measured object changes slowly over the narrow spectral window, the temperature corresponding to the normalized spectral radiation intensity for each window in the set temperature range is calculated using the Mahalanobis distance coefficient. The temperature error is less than 1.33% relative to thermocouple measurements when using this algorithm to perform temperature inversion on the experimental spectrum curves for different types of alloy samples. Furthermore, a two-dimensional spectral temperature field measurement platform was built, and the surface temperature fields of alloy samples were reconstructed using the narrow-band spectral window moving algorithm. The proposed algorithm is shown to provide high-precision inversion of the temperature field without presetting the emissivity model, which gives a new processing concept for the application of infrared spectral temperature measurements. |
| Author | Zheng, Kaifeng Liang, Jingqiu Wang, Chao Zhao, Yingze Wang, Weibiao Lv, Jinguang Tao, Jin Qin, Yuxin |
| Author_xml | – sequence: 1 givenname: Yingze surname: Zhao fullname: Zhao, Yingze – sequence: 2 givenname: Jinguang surname: Lv fullname: Lv, Jinguang – sequence: 3 givenname: Kaifeng surname: Zheng fullname: Zheng, Kaifeng – sequence: 4 givenname: Jin surname: Tao fullname: Tao, Jin – sequence: 5 givenname: Yuxin surname: Qin fullname: Qin, Yuxin – sequence: 6 givenname: Weibiao surname: Wang fullname: Wang, Weibiao – sequence: 7 givenname: Chao surname: Wang fullname: Wang, Chao – sequence: 8 givenname: Jingqiu surname: Liang fullname: Liang, Jingqiu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33771019$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.7449/2004/Superalloys_2004_707_712 10.1016/j.jallcom.2017.01.288 10.1364/AO.56.004654 10.1177/0003702816644757 10.1016/j.proci.2010.07.042 10.1007/s10765-008-0446-5 10.1063/1.5007225 10.1364/AO.56.008492 10.1016/j.measurement.2016.02.054 10.1021/ef401374y 10.1088/0957-0233/26/10/105203 10.1016/j.infrared.2016.11.014 10.1007/s10043-015-0155-9 10.1364/OE.25.030560 10.1016/j.infrared.2017.04.013 10.1364/OE.26.025706 10.1016/j.ijheatmasstransfer.2018.09.008 10.18178/ijmerr 10.1016/j.ijheatmasstransfer.2009.12.053 10.3390/en12112185 10.1016/j.engfailanal.2014.06.003 10.1364/AO.55.002169 10.1063/1.3596567 10.1038/s41377-019-0231-1 10.1016/j.infrared.2020.103273 10.1049/iet-rpg.2019.0119 10.1016/j.ijheatmasstransfer.2017.06.133 10.1115/1.4024678 10.1016/j.expthermflusci.2019.110017 10.1038/lsa.2012.24 10.1177/0003702818823239 10.1061/(ASCE)AS.1943-5525.0000296 10.1364/AO.43.001799 |
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| References | Sahlberg (oe-29-3-4405-R12) 2019; 73 Deep (oe-29-3-4405-R28) 2017; 56 Zhang (oe-29-3-4405-R34) 2016; 70 Wen (oe-29-3-4405-R30) 2010; 53 Liang (oe-29-3-4405-R13) 2018; 26 Talghader (oe-29-3-4405-R29) 2012; 1 Zhang (oe-29-3-4405-R23) 2020; 106 Garg (oe-29-3-4405-R2) 2013; 26 Zhao (oe-29-3-4405-R3) 2019; 128 Bouvry (oe-29-3-4405-R19) 2017; 83 Kong (oe-29-3-4405-R33) 2017; 703 Kumari (oe-29-3-4405-R5) 2014; 45 Liu (oe-29-3-4405-R17) 2019; 13 Zhang (oe-29-3-4405-R32) 2017; 114 Zhu (oe-29-3-4405-R8) 2020; 9 Gao (oe-29-3-4405-R27) 2015; 26 Yan (oe-29-3-4405-R26) 2013; 27 Sade (oe-29-3-4405-R10) 2004; 43 Sun (oe-29-3-4405-R25) 2011; 33 Ni (oe-29-3-4405-R9) 2017; 56 Wang (oe-29-3-4405-R21) 2018; 89 Lü (oe-29-3-4405-R7) 2016; 55 Okada (oe-29-3-4405-R1) 2004; 2004 Manara (oe-29-3-4405-R20) 2017; 80 Yuan (oe-29-3-4405-R31) 2009; 30 Ketui (oe-29-3-4405-R18) 2016; 86 Yan (oe-29-3-4405-R24) 2020; 112 Gao (oe-29-3-4405-R11) 2014; 23 Dewangan (oe-29-3-4405-R6) 2015; 4 Lin (oe-29-3-4405-R22) 2019; 12 Fu (oe-29-3-4405-R15) 2011; 82 Estevadeordal (oe-29-3-4405-R16) 2014; 136 Xing (oe-29-3-4405-R14) 2017; 25 |
| References_xml | – volume: 2004 start-page: 707 year: 2004 ident: oe-29-3-4405-R1 publication-title: Super alloy doi: 10.7449/2004/Superalloys_2004_707_712 – volume: 703 start-page: 125 year: 2017 ident: oe-29-3-4405-R33 publication-title: J. Alloys Compd. doi: 10.1016/j.jallcom.2017.01.288 – volume: 56 start-page: 4654 year: 2017 ident: oe-29-3-4405-R9 publication-title: Appl. Opt. doi: 10.1364/AO.56.004654 – volume: 70 start-page: 1717 year: 2016 ident: oe-29-3-4405-R34 publication-title: Appl. Spectrosc. doi: 10.1177/0003702816644757 – volume: 33 start-page: 735 year: 2011 ident: oe-29-3-4405-R25 publication-title: Proc. Combust. Inst. doi: 10.1016/j.proci.2010.07.042 – volume: 30 start-page: 227 year: 2009 ident: oe-29-3-4405-R31 publication-title: Int. J. Thermophys. doi: 10.1007/s10765-008-0446-5 – volume: 89 start-page: 054903 year: 2018 ident: oe-29-3-4405-R21 publication-title: Rev. Sci. Instrum. doi: 10.1063/1.5007225 – volume: 56 start-page: 8492 year: 2017 ident: oe-29-3-4405-R28 publication-title: Appl. Opt. doi: 10.1364/AO.56.008492 – volume: 86 start-page: 133 year: 2016 ident: oe-29-3-4405-R18 publication-title: Measurement doi: 10.1016/j.measurement.2016.02.054 – volume: 27 start-page: 6754 year: 2013 ident: oe-29-3-4405-R26 publication-title: Fuel doi: 10.1021/ef401374y – volume: 26 start-page: 105203 year: 2015 ident: oe-29-3-4405-R27 publication-title: Meas. Sci. Technol. doi: 10.1088/0957-0233/26/10/105203 – volume: 80 start-page: 120 year: 2017 ident: oe-29-3-4405-R20 publication-title: Infrared Phys. Technol. doi: 10.1016/j.infrared.2016.11.014 – volume: 23 start-page: 17 year: 2014 ident: oe-29-3-4405-R11 publication-title: Proc. SPIE doi: 10.1007/s10043-015-0155-9 – volume: 25 start-page: 30560 year: 2017 ident: oe-29-3-4405-R14 publication-title: Opt. Express doi: 10.1364/OE.25.030560 – volume: 83 start-page: 78 year: 2017 ident: oe-29-3-4405-R19 publication-title: Infrared Phys. Technol. doi: 10.1016/j.infrared.2017.04.013 – volume: 26 start-page: 25706 year: 2018 ident: oe-29-3-4405-R13 publication-title: Opt. Express doi: 10.1364/OE.26.025706 – volume: 128 start-page: 378 year: 2019 ident: oe-29-3-4405-R3 publication-title: Int. J. Heat Mass Transfer doi: 10.1016/j.ijheatmasstransfer.2018.09.008 – volume: 4 start-page: 216 year: 2015 ident: oe-29-3-4405-R6 publication-title: IJMERR doi: 10.18178/ijmerr – volume: 53 start-page: 2035 year: 2010 ident: oe-29-3-4405-R30 publication-title: Int. J. Heat Mass Transfer doi: 10.1016/j.ijheatmasstransfer.2009.12.053 – volume: 12 start-page: 2185 year: 2019 ident: oe-29-3-4405-R22 publication-title: Energies doi: 10.3390/en12112185 – volume: 45 start-page: 234 year: 2014 ident: oe-29-3-4405-R5 publication-title: Eng. Failure Anal. doi: 10.1016/j.engfailanal.2014.06.003 – volume: 55 start-page: 2169 year: 2016 ident: oe-29-3-4405-R7 publication-title: Appl. Opt. doi: 10.1364/AO.55.002169 – volume: 82 start-page: 064902 year: 2011 ident: oe-29-3-4405-R15 publication-title: Rev. Sci. Instrum. doi: 10.1063/1.3596567 – volume: 9 start-page: 1 year: 2020 ident: oe-29-3-4405-R8 publication-title: Light: Sci. Appl. doi: 10.1038/s41377-019-0231-1 – volume: 106 start-page: 103273 year: 2020 ident: oe-29-3-4405-R23 publication-title: Infrared Phys. Technol. doi: 10.1016/j.infrared.2020.103273 – volume: 13 start-page: 1833 year: 2019 ident: oe-29-3-4405-R17 publication-title: IET Renewable Power Generation doi: 10.1049/iet-rpg.2019.0119 – volume: 114 start-page: 1037 year: 2017 ident: oe-29-3-4405-R32 publication-title: Int. J. Heat Mass Transfer doi: 10.1016/j.ijheatmasstransfer.2017.06.133 – volume: 136 start-page: 031004 year: 2014 ident: oe-29-3-4405-R16 publication-title: J. Turbomachinery doi: 10.1115/1.4024678 – volume: 112 start-page: 110017 year: 2020 ident: oe-29-3-4405-R24 publication-title: Exp. Therm. Fluid Sci. doi: 10.1016/j.expthermflusci.2019.110017 – volume: 1 start-page: e24 year: 2012 ident: oe-29-3-4405-R29 publication-title: Light: Sci. Appl. doi: 10.1038/lsa.2012.24 – volume: 73 start-page: 653 year: 2019 ident: oe-29-3-4405-R12 publication-title: Appl. Spectrosc. doi: 10.1177/0003702818823239 – volume: 26 start-page: 422 year: 2013 ident: oe-29-3-4405-R2 publication-title: J. Aerosp. Eng. doi: 10.1061/(ASCE)AS.1943-5525.0000296 – volume: 43 start-page: 1799 year: 2004 ident: oe-29-3-4405-R10 publication-title: Appl. Opt. doi: 10.1364/AO.43.001799 |
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