Estimation of the aging grade of heat-resistant steel based on portable laser-induced breakdown spectroscopy
ZHANG Yong-sheng1,2,3, DONG Mei-rong*1,2,3, CAI Jun-bin1,2,3, LI Wei-jie4, LU Ji-dong1,2,3
1. School of Electric Power, South China University of Technology, Guangzhou 510640, China; 2. Guangdong ProvinceEngineering Research Center of High Efficient and Low Pollution, Guangzhou 510640, China; 3. Guangdong ProvinceKey Laboratory of Efficient and Clean Energy Utilization, Guangzhou 510640, China; 4. Zhongye South (Wuhan)Thermal Co., Ltd., Wuhan 430205, China
Abstract：The state of heat-resistant steel that has been subjected to high temperature and high pressure for a long time seriously, would affect the safe production of equipment systems. Traditional inspection methods have certain limitations in time and operation. The developed portable laser-induced breakdown spectroscopy (LIBS) equipment was applied to the failure diagnosis of heat-resistant steel, and combined with chemometric methods to predict and evaluate the aging grade of T91 steel. Firstly, the spectral characteristics of T91 samples with different aging grades were analyzed. The variation trend of the element characteristic spectral lines of samples of different surface states with the number of pulses was studied, so that the representative spectra of each sample were obtained. Then the spectral variables were selected by the K-fold-support vector machine-recursive feature elimination (K-SVM-REF) to improve the prediction of the model. The results showed that the classification accuracy of the support vector machine (SVM) prediction model based on spectral variable of feature selection compared with full spectrum variables was improved from 84.38% to 90.63%. In addition, the influence of the sample surface state on the performance of the model was also studied, which provided an effective basis and method for the practical measurement of the failure diagnosis of the metal heating surface with portable LIBS device developed.
张勇升, 董美蓉, 蔡俊斌, 李卫杰, 陆继东. 基于便携式激光诱导击穿光谱技术的耐热钢老化等级评估[J]. 冶金分析, 2020, 40(12): 86-93.
ZHANG Yong-sheng, DONG Mei-rong, CAI Jun-bin, LI Wei-jie, LU Ji-dong. Estimation of the aging grade of heat-resistant steel based on portable laser-induced breakdown spectroscopy. , 2020, 40(12): 86-93.
Fournier B,Sauzay M,Barcelo E,et al.Creep-fatigue interactions in a 9 Pct Cr-1 Pct Mo martensitic steel:part II.microstructural evolutions[J].Metallurgical & Materials Transactions A,2009,40:330-341.
[2]
Sheta S,Afgan M S,Hou Z,et al.Coal analysis by laser-induced breakdown spectroscopy:a tutorial review[J].Journal of Analytical Atomic Spectrometry,2019,34(6):1047-1082.
[3]
Cavalcanti G H,Teixeira D V,Legnaioli S,et al.One-point calibration for calibration-free laser-induced breakdown spectroscopy quantitative analysis[J].Spectrochimica Acta Part B:Atomic Spectroscopy,2013,87(9):51-56.
[4]
Khajehzadeh N,Haavisto O,Koresaar L.On-stream and quantitative mineral identification of tailing slurries using LIBS technique[J].Minerals Engineering,2016,98:101-109.
[5]
Giorgio S,Nicola S.Laser-induced breakdown spectroscopy (LIBS) to measure quantitatively soil carbon with emphasis on soil organic carbon.a review[J].Analytica Chimica Acta,2016,938:7-17.
[6]
Death D L,Cunningham A P,Pollard L J.Multi-element analysis of iron ore pellets by laser-induced breakdown spectroscopy and principal components regression[J].Spectrochimica Acta Part B:Atomic Spectroscopy,2008,63:763-769.
[7]
Sturm V,Schmitz H U,Reuter T,et al.Fast vacuum slag analysis in a steel works by laser-induced breakdown spectroscopy[J].Spectrochimica Acta Part B:Atomic Spectroscopy,2008,63:1167-1170.
[8]
Abdel-Salam Z,Abdelhamid M,Khalil S M,et al.LIBS new application:determination of metallic alloys surface hardness[C]//AIP Conference Proceedings.Cairo:Amer.Inst.Physics,2009,1172:49-52.
[9]
戴沅,董璇,钟万里,等.12Cr1MoV晶粒度等级与激光诱导击穿光谱特性的关联性研究[J].中国激光,2014,41(4):269-274.DAI Ruan,DONG Xuan,ZHONG Wan-li,et al.Study of the relevance between different grain size of 12Cr1MoV and characteristics of laser induced breakdown spectroscopy[J].Chinese Journal of Lasers,2014,41(4):269-274.
[10]
Porizka P,Rocnakova Klus J,Prochazka D,et al.Estimating the grade of Mg corrosion using laser-induced breakdown spectroscopy[J].Journal of Analytical Atomic Spectrometry,2015,30(10):2099-2106.
[11]
YAO Shun-chun,DONG Mei-rong,LU Ji-dong,et al.Correlation between grade of pearlite spheroidization and laser induced spectra[J].Laser Physics,2013,23(12):125702.
[12]
Daniel M,Rémi L,éric Proulx.Influence of particle size and mineral phase in the analysis of iron ore slurries by laser-induced breakdown spectroscopy[J].Spectrochimica Acta Part B:Atomic Spectroscopy,2007,62(12):1575-1581.
[13]
LI Jun,LU Ji-dong,DAI Yuan,et al.Correlation between aging grade of T91 steel and spectral characteristics of the laser-induced plasma[J].Applied Surface Science,2015,346:302-310.
[14]
Ismail M A,Imam H,Elhassan A,et al.LIBS limit of detection and plasma parameters of some elements in two different metallic matrices[J].Journal of Analytical Atomic Spectrometry,2004,19(4):489-494.
[15]
潘圣华,陆继东,姚顺春,等.金相组织对激光诱导钢铁等离子体的影响[J].中国激光,2010,37(8):2126-2130.PAN Sheng-hua,LU Ji-dong,YAO Shun-chun,et al.Impact of metallurgical structure on laser induced steel plasma[J].Chinese Journal of Lasers,2010,37(8):2126-2130.
[16]
Yao S,Lu J,Chen K,et al.Study of laser-induced breakdown spectroscopy to discriminate pearlitic/ferritic from martensitic phases[J].Applied Surface Science,2011,257(7):3103-3110.
[17]
HUANG Jian-wei,DONG Mei-rong,LU Sheng-zi,et al.Estimation of the mechanical properties of steel via LIBS combined with canonical correlation analysis (CCA) and support vector regression (SVR)[J].Journal of Analytical Atomic Spectrometry,2018,33:720-729.
[18]
Huang J,Dong M,Lu S,et al.A hybrid model combining wavelet transform and recursive feature elimination for running state evaluation of heat-resistant steel using laser-induced breakdown spectroscopy[J].Analyst,2019,144:3736-3745.
[19]
LU Sheng-zi,SHEN Shen,HUANG Jian-wei,et al.Feature selection of laser-induced breakdown spectroscopy data for steel aging estimation[J].Spectrochimica Acta Part B:Atomic Spectroscopy,2018,150:49-58.
[20]
LU Sheng-zi,DONG Mei-rong,HUANG Jian-wei,et al.Estimation of the aging grade of T91 steel by laser-induced breakdown spectroscopy coupled with support vector machines[J].Spectrochimica Acta Part B:Atomic Spectroscopy,2018,140:35-43.
[21]
Viswanathan R,Coleman K,Rao U.Materials for ultra-supercritical coal-fired power plant boilers[J].International Journal of Pressure Vessels and Piping,2006,83:778-783.
[22]
李久林,梁新邦.GB/T 2039—1997金属拉伸蠕变及持久试验方法国家标准编制说明[J].冶金标准化与质量,1998(3):4-8.LI Jiu-lin,LIANG Xin-bang.GB/T 2039—1997 metallic materials-creep and stress-rupture test in tension[J].Metallurgical Standardization & Quality,1998(3):4-8.
[23]
LU Sheng-zi,DONG Mei-rong,CHEN Qin-fei,et al.Temporal-spatial resolved laser-induced breakdown spectroscopy of T91 steel of different aging grades[J].Spectrochimica Acta Part B:Atomic Spectroscopy,2019,151:1-11.
Swami A,Jain R.Scikit-learn:machine learning in python[J].Journal of Machine Learning Research,2012,12(10):2825-2830.
[26]
Patel D N,Ravi Pratap Singh,Raj K,et al.Craters and nanostructures with laser ablation of metal/metal alloy in air and liquid[J].Applied Surface Science,2014,288:550-558.
