科研之友
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摘要
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.
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