Power transformer safe operation depends critically on oil-paper insulation condition. Traditional insulation aging detection approaches possess significant drawbacks, including long testing cycles, destructive procedures, and insufficient precision. Raman spectroscopy offers a rapid, non-destructive alternative by capturing molecular vibration characteristics associated with aging byproducts. However, conventional machine learning algorithms exhibit low efficiency, high computational cost, and weak generalization capabilities when processing high-dimensional Raman spectral data. Advanced two-dimensional convolutional neural networks demand excessive computational resources through artificial dimensionality expansion. To address these limitations, an intelligent insulation aging assessment approach utilizing a one-dimensional convolutional neural network (1D-CNN) integrated with a Dilated Inception-ResNet module is developed. The goal is to achieve accurate, rapid, and robust aging stage classification by automatically extracting multi-scale spectral features while mitigating gradient vanishing issues commonly found in deep networks, balancing computational cost and diagnostic performance effectively.

Accelerated thermal aging experiments generated three hundred oil-paper insulation samples. Mineral oil and kraft paper mixtures underwent continuous heating at 120 degrees Celsius for up to 480 hours. Samples were collected every 24 hours. Gas chromatography measured furfural content to establish ground truth labels, categorizing the samples into four distinct aging stages: initial, mid-term, late, and final. A portable Raman spectrometer collected molecular vibration spectra from the prepared samples. The laser power was set at 300 milliwatts with an excitation wavelength of 784.711 nanometers and an integration time of 500 milliseconds. Raw Raman spectral data underwent a rigorous serial preprocessing pipeline. Savitzky-Golay smoothing eliminated high-frequency noise interference. Subsequently, the adaptive iteratively reweighted Penalized Least Squares algorithm corrected baseline drift caused by fluorescence background interference. This preprocessing generated high-quality spectral data inputs. An enhanced 1D-CNN architecture was constructed. The core innovation involved designing a Dilated Inception-ResNet module. The network initially utilized a standard one-dimensional convolutional layer and max-pooling layer for preliminary feature mapping and dimensionality reduction. Two cascaded Dilated Inception-ResNet modules followed. Each module incorporated four parallel feature processing branches. The first branch utilized point convolutions for channel dimension linear transformations. The second and third branches applied a compress-expand strategy, using initial 1×1 convolutions followed by 1×3 and 1×5 one-dimensional dilated convolutions, respectively, to capture medium and long-range temporal dependencies without increasing parameter count. The fourth branch utilized max-pooling for significant feature retention. To prevent network degradation and gradient vanishing, adaptive residual connections linked the inputs and outputs of these modules, utilizing a 1×1 convolution for dimension matching when necessary. The network concluded with global flattening and fully connected layers for classification. Training utilized the AdamW optimizer and cross-entropy loss function.

Repeated random sampling validation experiments evaluated baseline model performance. The standard 1D-CNN achieved an average accuracy of 89.83% and a recall of 89.26%, outperforming traditional support vector machine and K-nearest neighbor classifiers. This demonstrated the inherent advantage of deep learning in automatically extracting representations from complex, high-dimensional spectral data without relying on manual feature engineering. Ablation studies verified the efficacy of the proposed Dilated Inception-ResNet architecture. The enhanced model achieved a maximum test set classification accuracy of 96.67%. This represented a significant absolute accuracy improvement of 6.67% over the original 1D-CNN and 3.34% over a standard Inception-1DCNN model without dilated convolutions or residual connections. The loss function curve demonstrated rapid and stable convergence within twenty epochs, confirming that the adaptive residual connections successfully facilitated smooth gradient backpropagation and eliminated gradient vanishing problems. Computational complexity analysis revealed that while parameters and floating-point operations increased moderately, the single-sample inference time remained exceptionally low at 0.1424 milliseconds, fully satisfying real-time monitoring requirements. Further extensive testing assessed model robustness and generalization capability under suboptimal data conditions. Three dataset configurations with varying total sample sizes and class distributions evaluated performance across different train-test split ratios. For a highly imbalanced dataset containing 230 samples, the proposed model maintained an average accuracy exceeding 92.5% across all split ratios. For a constrained small dataset containing only 170 samples, the average accuracy remained robust above 91.7%. These consistent performance metrics across varied data scenarios proved the multi-scale feature extraction mechanism successfully learned intrinsic physicochemical aging features rather than relying on statistical class distributions. The network architecture effectively prevented over-attention to majority classes and ensured reliable recognition of minority class samples representing critical severe aging stages.

The proposed Dilated Inception-ResNet 1D-CNN model provides a superior, non-destructive, and rapid diagnostic solution for oil-paper insulation aging assessment. Serial preprocessing techniques combining Savitzky-Golay smoothing and adaptive iteratively reweighted Penalized Least Squares algorithms significantly enhance Raman spectral data quality. The integration of multi-branch parallel dilated convolutions expands receptive fields for multi-scale feature extraction without escalating computational costs, while adaptive residual connections ensure stable deep network training. The model demonstrates exceptional classification accuracy, stability, and robustness, even when processing small or heavily imbalanced datasets. This intelligent diagnostic framework offers reliable technical support for transformer condition monitoring, predictive maintenance scheduling, and power system reliability assurance.