光纤传能的移动式激光诱导击穿光谱钢铁快速检测与分类

为了实现工业现场对特种钢材的快速检测与种类识别，采用基于光纤传能的移动式激光诱导击穿光谱（LIBS）样机对14种特种钢材进行光谱数据的采集与分析，采用预选谱线并遍历组合的降维方法与支持向量机(SVM)相结合的算法对特钢材料的光谱进行快速分类。分别将原始光谱数据、归一化处理后的光谱数据、归一化处理+遍历组合优选谱线数据作为SVM分类模型的输入向量，并对比了不同输入向量下模型对特钢识别的准确度。结果表明：在事先选出的51条特征谱线作为输入变量的基础上，归一化光谱数据作为SVM分类模型的输入特征时，识别准确度达到95.71%，明显高于使用原始光谱数据作为输入向量时SVM分类模型的准确度11.43%。进一步地，使用MATLAB程序遍历谱线组合，通过遍历各种谱线组合选出最优的输入谱线组合，当优选6条特定的谱线时，对特钢种类识别的准确度达到100%，且建模速度也有相应提升。可以看出，当预选出大量常见特征数据时，机器自动选取特征与人工挑选谱线相比，具有明显优势，基于此降维方法的SVM算法模型在LIBS快速分类技术中具有很好的工业应用前景。

Rapid Classification of Steel by a Mobile Laser-Induced Breakdown Spectroscopy Based on Optical Fiber Delivering Laser Energy

In order to realize the industrial on-site rapid detection and identification for special steel, a mobile laser-induced breakdown spectroscopy prototype based on optical fiber delivering laser energy is adopted in this experiment to collect the spectral data of 14 special sheets of steel. The spectra of special steels were rapidly classified via dimensionality reduction in which pre-selected spectral lines were traversed, combined with a support vector machine (SVM).In the experiment, original spectral data, normalized spectral data and normalized spectral data after traversed were used as the input vectors of the SVM classification model, and the recognition accuracy of the model for special steels under different input vectors was compared. The results show that on the basis that more than 51 spectral lines were selected as input variables, the recognition accuracy of normalized spectral data as input variables for steels reaches 95.71%. It is significantly higher than 11.43%, whose accuracy was used raw spectral data as the input vector. Further, the MATLAB program was used to traverse the spectral line combination to choose the optimal input features. When 6 specific spectral lines were selected, the accuracy of special steels recognition reached 100%, and the modeling speed was also improved accordingly. It can be seen that when a large number of common feature data are pre-selected, automatic feature selection by machine has obvious advantages over the spectral line of manual selection. The SVM algorithm based on this dimension reduction method has a good industrial application prospect in LIBS rapid classification technology.