耐热钢微观组织及机械性能会随着服役过程发生退化，对老化状态的实时快速监测对安全运行及生产具有重要意义。基于便携式激光诱导击穿光谱（LIBS）设备对获取的T91光谱特征进行降维并优化了老化等级评估模型，实现了对T91耐热钢老化等级的快速诊断。分别采用主成分分析与线性判别式分析（LDA）的降维方法，对光谱特征进行优化精简。而后基于降维后的数据，进一步采用K最近邻算法和支持向量机（SVM）算法来建立金属老化等级评估模型，讨论了建模关键参数选择对模型性能的影响。结果表明，经过LDA降维的光谱数据能实现更好的聚类分布，可有效提高评估模型的准确率。同时，应用LDA-SVM模型能获得最高的老化等级评估准确度，达94.58%。所采用的模型建模方法可有效实现基于便携式LIBS的T91耐热钢老化等级评估。

Microstructure and mechanical properties of heat-resistant steel will deteriorate during the service process. The real-time monitoring of the aging state is of great significance for safe operation and production. In this study, a portable laser-induced breakdown spectroscopy (LIBS) device is used to quickly diagnose the aging grade of T91 steel, while the obtained spectral features are dimensionally reduced and the modeling method is optimized. Principal component analysis (PCA) and linear discriminant analysis (LDA) are used to optimize and simplify the spectral features. Finally, after dimensionality reduction, the data are used to evaluate the aging grade model based on the K-nearest neighbor and the support vector machine (SVM) algorithms. Further, the influence of key parameter selection on the model performance is studied. The results show that the spectral data reduced by LDA can achieve a better clustering distribution and improve the accuracy of the evaluation model. In addition, the LDA-SVM model can achieve 94.58% accuracy, which is the highest among all the mentioned aging grade evaluation models. The result demonstrates that the modeling method can efficiently realize the aging grade evaluation of T91 steel based on portable LIBS.