## 1 Introduction
[11] used a variational mode decomposition algorithm to obtain vibration fault signal components and calculated single-component multi-scale entropy to form feature vectors, further optimizing the features for fault identification. [PERSON] et al. [12] defined the vibration characteristics of high-voltage circuit breakers based on the integrated empirical mode decomposition method and diagnosed mechanical faults of high-voltage circuit breakers using support vector machines. The above methods have achieved satisfactory results in fault diagnosis, feature extraction, and state prediction. However, such methods often rely on manual means to extract features and expert experience, and are often shallow learning-based, lacking the ability to process large-scale data. In order to overcome the influence of manual extraction methods on the results, some deep learning algorithms have begun to play a role in transformer fault diagnosis, among which convolutional neural networks (CNN) have received increasing attention for their adaptive feature extraction ability. [PERSON] et al. [13] proposed a novel convolutional neural network based on LeNet-5 for fault diagnosis. This method converts signals into two-dimensional images and extracts features from the converted two-dimensional images, eliminating the influence of manual feature extraction. [PERSON] et al. [14] first converts the fault vibration signal into a two-dimensional image form through recursive encoding, and then uses a deep separation convolutional neural network (DSD-CNN) to extract features from these images, ultimately achieving accurate fault diagnosis. Reference [15] proposes a new deep CNN transformer model that can automatically detect the type, phase, and location of faults in power lines. The deep residual network (ResNet), as an improved version of a convolutional neural network, can effectively solve the problems of time gradient explosion and network degradation caused by the increase in network layers, and is also widely used in transformer fault diagnosis. [PERSON] et al. [16] decompose the vibration signals of the contactor into intrinsic modal components through variational mode decomposition and then convert them into modal time-frequency diagrams, which are input into ResNet50 for fault diagnosis. [PERSON] et al. [17] convert one-dimensional perturbation signals into two-dimensional trajectory circles with distinct shape features and input them into ResNet for feature learning and classification recognition. [PERSON] et al. [18] used Markov transform fields to convert one-dimensional signals into two-dimensional images and established a deep residual network for feature extraction. [PERSON] et al. [19] proposed a multi-bol loosening identification method based on a time-frequency graph and ResNet50, and verified through experiments that the method can identify fault types with reasonable accuracy, computational efficiency, and robustness. Although deep learning methods have achieved significant results in feature extraction and pattern recognition, existing deep learning methods still face some challenges in the field of transformer fault diagnosis. Especially in terms of feature extraction, traditional methods often struggle to capture weak features and complex changes in fault signals. In addition, the diagnostic performance of existing methods in noisy environments is not satisfactory, and they are prone to interference and misdiagnosis. Therefore, how to develop an accurate, efficient, and noise-robust transformer fault diagnosis method has become a hot and difficult research topic. In order to overcome the shortcomings of existing deep learning methods in transformer fault diagnosis, this paper proposes a transformer fault diagnosis method based on the residual network, a multi-head attention mechanism, and ensemble empirical mode decomposition (EEMD). This method first uses EEMD to denoise the original fault signal and extract purer signal components.