Research on intelligent diagnosis methods for transformer vibration signals combining CNN

Artificial intelligence technologies are widely applied in the field of power equipment condition monitoring, with convolutional neural networks (CNNs) demonstrating strong adaptability in extracting features from non-stationary vibration signals. This study aims to enhance the accuracy and real-time performance of transformer fault diagnosis by constructing an intelligent diagnostic method based on CNNs. Methodologically, raw vibration signals under multiple operating conditions were first collected using triaxial accelerometers. Short-Time Fourier Transform (STFT) was applied to construct a three-channel two-dimensional time-frequency map. Subsequently, a multi-layer CNN architecture was designed, integrating residual connections and an adaptive learning rate decay strategy to enhance the model's feature modeling capability in noisy environments. A real-world dataset from a 220kV main transformer was employed for training and validation. Results demonstrate that the constructed model achieves accuracy ranges between 90% and 96% across five operating conditions, with a maximum F1 score of 0.95, significantly outperforming comparison models including 1D-CNN, SVM, and LSTM. Findings indicate that this approach effectively enhances fault identification accuracy and stability, providing a foundational model and strategic support for subsequent online monitoring system deployment.