高级检索

    基于近红外光谱的输电线钢芯腐蚀原位检测方法

    In-Situ Detection Method for Steel Core Corrosion in Power Transmission Lines Based on Near-Infrared Spectroscopy

    • 摘要: 基于近红外光谱技术,提出了一种输电线钢芯腐蚀原位检测方法。首先,通过近红外检测获得4类不同腐蚀状态输电线表面的近红外光谱;然后,对比分析获得最佳光谱数据预处理方法,通过潜在投影图(LPG)选择了最佳建模波长,并结合主成分分析(PCA)降维数据和鹈鹕优化算法(POA)优化参数建立了基于支持向量机回归(SVR)的腐蚀状态分类识别模型;最后,采用能谱分析数据验证模型对腐蚀状态识别的准确性。结果表明:采用标准正态变量处理和Savitzky-Golay平滑预处理可以达到99.16%的最大方差解释率,通过LPG筛选出了10个最佳波长,结合最佳光谱数据预处理方法与最佳波长并利用PCA得到4类样本的可视化聚类结果,将PCA二维得分数据输入POA-SVR分类模型,得到最终分类准确率高达96.43%。

       

      Abstract: Based on near-infrared spectroscopy, an in-situ detection method for the corrosion of the steel core in power transmission lines was proposed. Firstly, the near-infrared spectra of the surfaces of four types of power transmission lines with different corrosion states were obtained through near-infrared detection. Then, the best spectral data preprocessing method was obtained through comparative analysis. The optimal wavelengths for modeling were selected by the latent projection graph (LPG). A recognition model of corrosion state classification based on support vector regression (SVR) was established by combining principal component analysis (PCA) dimension-reduced data and Pelican optimization algorithm (POA) optimized parameters. Finally, the accuracy of the corrosion state recognition model was verified using energy spectrum analysis data. The results show that a maximum variance explained rate of 99.16% could be achieved using standard normal variable processing and Savitzky-Golay smoothing preprocessing. And 10 optimal wavelengths were selected out by LPG. The visual clustering results of the 4 classes of samples were obtained by combining the best spectral data preprocessing method with the best wavelengths and using PCA. The final classification accuracy rate was 96.43% by inputting PCA 2D score data into POA-SVR classification model.

       

    /

    返回文章
    返回