# A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis
## 1 Introduction
Nowadays, it is widely used in various fields such as speech recognition, video classification and stock prediction. At present, there are still few relevant studies on the fusion of deep learning models. Some papers have tried to integrate multiple deep learning frameworks into the same system to improve the final recognition accuracy [28]. But these deep learning frameworks are often frameworks that deal with data of the same type. For example, [29; 30] were both about parallel fusions between CNN frameworks. Most of the relevant research on the combination of CNN and LSTM for diagnosis combined CNN and LSTM together in a series. For example, [31] used CNN to extract image features, obtaining feature sequences, and used LSTM to classify them. However, it is rare to comprehensively consider the complementary relationship between CNN and LSTM for extracting different features, especially in transformer DGA fault diagnosis modeling. DGA-based transformer fault diagnostic data are often single-time data [32], so the fault tolerance of the data is not good. Moreover, there may be some noises in the real application environment, which makes it worth studying the anti-interference ability and robustness of diagnostic methods [33]. The data visualization method has strong anti-interference performance. Therefore, converting numerical data into images and processing them with CNN can improve the fault tolerance of the data. The LSTM network can not only process point data, but also sequence data. It can be used in transformer fault diagnosis to support the diversity of input data type, thereby improving the generalization ability of the model. In addition, the difficulties in DGA fault data acquisition, the uneven distribution of transformer fault types, the continuous improvement of equipment manufacturing technology and fault monitoring methods may lead to the imbalance and shortage of fault samples [34]. Using a model with many parameters on such an imbalanced small training dataset will increase the risk of overfitting and weaken the model's ability to generalize outside the sample dataset. Therefore, it is necessary to consider the problems of uneven and insufficient samples in the research. At present, the most common solution to the problem of imbalanced data is oversampling, which has become the simplest and most reliable method to solve the imbalance and deficiency of a specific dataset [35]. The adaptive synthetic oversampling (ADASYN) algorithm is an improved technique for the synthetic minority over-sampling technique (SMOTE). According to the learning difficulty level of insufficient samples, the algorithm uses adaptive weight for different types of insufficient samples instead of using homogeneous weight. That is, if it is more difficult to understand a specific minority sample, more synthetic data about that sample will be generated. On the other hand, for the problem of insufficient data, transfer learning technology can be introduced in the CNN diagnostic framework. Transfer learning can map the knowledge in the B domain to the A domain [36; 37; 38]. With the transfer learning method, a small amount of data can still achieve a high diagnostic accuracy [39; 40]. Generally speaking, there still exist the following problems in current transformer DGA fault diagnostic methods:  
* The diagnostic accuracy, adaptability and anti-interference ability of traditional methods still need to be improved;
* The DGA fault dataset is in fact imbalanced and insufficient, which may have a bad effect on fault diagnosis modeling;
* The sensitivity difference to fault features of deep learning models as well as their fusion have not been taken into consideration in recent researches. In order to further improve the current transformer DGA fault diagnostic effect, this paper proposes a deep parallel diagnostic method for transformers. The main work and innovations of this article are as follows:  
* A deep parallel diagnostic method for power equipment is proposed.