A novel thickness measurement NIR spectrometry for surface insulation coating of silicon steel based on discrete binary particle swarm optimization (DBPSO) algorithm is presented. First, we used NIR spectrometer to collect the NIR spectra of insulation coating of silicon steel, and then, DBPSO algorithm was used to select the optimal wavelength variates and composed a new spectra set Last, the authors created the thickness quantitative analysis model using kernel partial least square algorithm. The experimental results show that the absolute error range analyzed by created model was from -0.12 to 0.19 μm, and the maximal relative error was 14. 31%, which completely met the practical measurement need. The research indicates that DBPSO is effective wavelength selection methods, which can efficiently select the wavelength variates carrying more useful information, improve the analysis accuracy and speed. And the NIR spectroscopy is an effective measurement method for thickness analysis of silicon steel insulation coating.%提出一种基于粒子群优化算法实现的硅钢涂层厚度近红外光谱检测新方法.首先,采用近红外光谱仪采集获得了硅钢表面绝缘涂层的近红外光谱,然后,采用离散粒子群算法筛选出近红外光谱数据的最佳波长变量并组成新的光谱数据,最后,建立涂层厚度的核偏最小二乘定量分析模型.实验显示,所建定最分析模型对检验样本分析的绝对误差范围为-0.12～0.19μm,最大相对误差为14.31％,完全符合现场检验需要.研究表明,离散粒子群算法可以有效地筛选出携带更多有用信息的波长变量,提高定量分析模型的分析准确度和速度,是一种有效的近红外光谱波长筛选方法,同时,近红外光谱法也是一种有效的硅钢绝缘涂层厚度检测方法.
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