Fault Diagnosis Method for Excitation Dry-Type Transformer Based on Multi-Channel Vibration Signal and Visual Feature Fusion.

To address the limitations of existing fault diagnosis methods for excitation dry-type transformers, such as inadequate utilization of multi-axis vibration data, low recognition accuracy under complex operational conditions, and limited computational efficiency, this paper presents a lightweight fault diagnosis approach based on the fusion of multi-channel vibration signals and visual features. Initially, a multi-physics field coupling simulation model of the excitation dry-type transformer is developed. Vibration data collected from field-installed three-axis sensors are combined to generate typical fault samples, including normal operation, winding looseness, core looseness, and winding eccentricity. Due to the high dimensionality of vibration signals, the Symmetrized Dot Pattern (ISDP) method is extended to aggregate and map time- and frequency-domain information from the x-, y-, and z-axes into a two-dimensional feature map. To optimize the inter-class separability and intra-class consistency of the map, Particle Swarm Optimization (PSO) is employed to adaptively adjust the angle gain factor (η) and time delay coefficient (t). Keypoint descriptors are then extracted from the map using the Oriented FAST and Rotated BRIEF (ORB) feature extraction operator, which improves computational efficiency while maintaining sensitivity to local details. Finally, an efficient fault classification model is constructed using an Adaptive Boosting Support Vector Machine (Adaboost-SVM) to achieve robust fault mode recognition across multiple operating conditions. Experimental results demonstrate that the proposed method achieves a fault diagnosis accuracy of 94.00%, outperforming signal-to-image techniques such as Gramian Angular Field (GAF), Recurrence Plot (RP), and Markov Transition Field (MTF), as well as deep learning models based on Convolutional Neural Networks (CNN) in both training and testing time. Additionally, the method exhibits superior stability and robustness in repeated trials. This approach is well-suited for online monitoring and rapid diagnosis in resource-constrained environments, offering significant engineering value in enhancing the operational safety and reliability of excitation dry-type transformers.