## 2 Research Status  
### Current Research Status of Fault Diagnosis Methods  
Fault diagnosis technology is a technique that monitors and analyzes the operational status of equipment to determine whether it is functioning normally or abnormally in its entirety or specific parts. It categorizes the abnormalities and faults that occur in the equipment and pinpoints the faulty components. Currently, the mainstream fault diagnosis technologies are mainly divided into physical model-based diagnosis methods [1], statistics-based diagnosis methods [2], and artificial intelligence-based diagnosis methods.  
(1) Physical models  
The diagnosis method based on a physical model usually has high diagnostic accuracy but lacks universality. It requires that the mathematical model of the object system be known, and as the structures of various equipment become increasingly complex and integrated, it is difficult to establish accurate mechanistic models. Therefore, the development and promotion of fault diagnosis methods based on physical models have been limited to a certain extent, and there is also less related research.  
Statistical models  
The statistical model-based fault diagnosis method necessitates neither a profound comprehension of the equipment or the system's structure and principles nor the establishment of intricate mechanisms or mathematical models, thus exhibiting high universality. However, it lacks clarity in the physical significance of diagnosed faults, offers limited interpretation, and possesses slightly lower diagnostic accuracy compared to methods rooted in physical models. The diagnosis method based on the artificial network can excavate the fault knowledge contained in the data by analyzing massive amounts of data and self-learning to realize fault diagnosis, which has stronger explanatory properties than the first two diagnostic methods.  
Fault diagnosis methods based on artificial intelligence can be divided into fault diagnosis methods based on expert systems [3], diagnosis methods based on shallow machine learning [4; 5], and fault diagnosis methods based on deep learning [6; 7]. The diagnosis method based on the expert system uses expert knowledge and experience to form a knowledge base, so the diagnostic model has the judgment ability similar to that of experts and can take into account the uncertain factors in the future and the special situation of the diagnostic object, but it requires a large amount of knowledge accumulation and revision, and it is difficult to establish a perfect diagnostic knowledge base. Both shallow machine learning and deep learning-based fault diagnosis methods rely on their feature extraction capabilities to mine the hidden information from the data to complete the fault diagnosis work, but with the increasing amount and dimension of data, the deep learning method has better performance than the shallow machine learning method [8]. Benefiting from the massive device state detection data and the rapid development of artificial neural networks, deep learning has been widely used in the field of fault diagnosis due to its excellent feature learning ability.  
The above research indicates that due to the lack of the need for a deep understanding of the precise physical model of the diagnostic object or system and the widespread application of data acquisition technology, fault diagnosis techniques based on deep learning have garnered the most widespread attention in related fields due to their high accuracy. Therefore, this article takes the box-type substation, a key piece of equipment in the distribution network, as an example to conduct research on fault diagnosis methods for distribution network equipment based on deep learning methods.