电力电缆绝缘材料型号的近红外光谱快速识别方法研究

Fast and accurate identification of insulation material types can prevent the mixing and misuse of raw materials,which is a key part of quality control in cable production. The traditional method using Fourier Transform Infrared Spectroscopy (FTIR) for sampling inspection has several drawbacks, including high cost, low efficiency, and poor adaptability to field conditions. It is difficult to meet the need for fast and full inspection of raw materials. This paper proposes a method for fast identification of cable insulation material types by combining near-infrared spectroscopy with a one-dimensional convolutional neural network (1D-CNN). Six common cable insulation materials were used as the research objects. Their spectral data were collected using a near-infrared spectrometer, and a 1D-CNN model with two convolution-pooling units was built. The model leverages its ability to extract local features from high-dimensional near-infrared spectral data, thereby identifying spectral differences between various material types. Based on this, various spectral preprocessing methods were applied to remove unwanted interference. The modeling performance under each strategy was compared systematically, and the second derivative of the spectrum using Savitzky-Golay smoothing was found to be the best preprocessing method. Bayesian optimization was introduced to adjust key model parameters and improve recognition accuracy automatically. The optimized model achieved an identification accuracy of 95.00%,a weighted average precision of 95.07%, a weighted average recall of 95.00%, and a weighted average F1 score of 0. 950 4,which are significantly better than traditional machine learning models. The results show that combining the 1D-CNN model with near-infrared spectroscopy enables fast, accurate,and non-destructive identification of cable insulation material types, with strong potential for field application. This study provides a reliable solution for the rapid screening and testing of cable insulation materials on a large scale,offering strong technical support for building an intelligent quality control system throughout the entire insulation material process.