[PERSON]  
1 School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China; [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (S.T. ); [EMAIL_ADDRESS] (N.L. ); [EMAIL_ADDRESS] (X.S. ); [EMAIL_ADDRESS] (Y.H. ); [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (W.Z.) Zhipeng Ma  
1 School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China; [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (S.T. ); [EMAIL_ADDRESS] (N.L. ); songxin@stu. The Raman spectroscopy vector of oil-paper insulation is nearly thousand-dimensional, containing excessive information that cannot be used to differentiate the aging stages of the insulation. This increases the burden of data storage and the complexity of subsequent aging stage discrimination models, thereby reducing the discrimination accuracy. In this type of problem, convolutional neural network (CNN) algorithms with feature-extraction capabilities have shown good discriminative abilities for high-dimensional spectra. However, CNN algorithms require complex image-encoding processes, which undoubtedly decrease the speed of discriminating the aging stages of oil-paper insulation [12]. Therefore, Raman spectroscopy feature extraction based on dimensionality transformation remains significant. By employing a diagnostic model that combines feature extraction using random forest and grid-search-optimized support vector machines (SVM), the classification accuracy for diagnosing the four aging states of oil-paper insulation was 91.6% [13]. Using wavelet scattering networks for feature extraction and Gaussian radial basis function (RBF) kernel SVM for classification, the diagnostic accuracy for classifying the three aging states was 90% [14]. Utilizing feature extraction with linear discriminant analysis (LDA) and constructing a diagnostic model using naive Bayes, the discrimination accuracy for classifying the four aging states was 90% [15]. Employing an optimized K-nearest neighbors (KNN) algorithm to construct a diagnostic model for classifying the aging days into twelve categories, the diagnostic accuracy for oil-paper insulation was 87.92% [16]. Existing research indicates that dimensionality transformation for extracting Raman spectroscopy features remains the primary method for reducing the information capacity. However, the current method for diagnosis of the aging stages of oil-paper insulation still faces challenges such as low discrimination accuracy and a limited number of aging state categories. To address the aforementioned issues, this paper introduces Isomap for feature extraction of Raman spectroscopy samples and combines it with XGBoost for the discrimination of aging states in oil-paper insulation. Firstly, the samples were classified into four and ten aging states based on the DP of the insulation paper. Next, a self-built insulation oil Raman spectroscopy detection platform was used to conduct Raman spectroscopy analysis on 175 sample groups. The resulting 953-dimensional Raman spectroscopy vectors were transformed using PCA, MDS, and Isomap. The distinguishing performance of the three feature-extraction methods was compared in the visualized dimensions. It was found that Isomap, due to the unique characteristics of manifold dimension transformation, can enhance the differentiation of aging state samples. Finally, based on the feature extraction, the XGBoost algorithm optimized with Bayesian methods (BO-XGBoost) was introduced to construct a sample discrimination model based on DP. The discrimination accuracies of the three feature-extraction methods were compared, and it was further concluded that the manifold dimension transformation of Raman spectra from oil-paper insulation, compared to traditional linear transformations (PCA, MDS), is more helpful in improving the subsequent discrimination accuracy.