# Research on State Diagnosis Methods of UHF Partial Discharge Sensors Based on Improved ViT
## 1 Introduction  
UHF (Ultra High Frequency) partial discharge sensors play a pivotal role in the monitoring systems of substations, which are integral to modern electrical grid infrastructure. These sensors are fundamental not only for real-time monitoring and measurements within substations but also in automation control and information management systems [1]. The data they generate serve a variety of purposes, ranging from data processing and analysis to system operation and fault detection, enhancing the reliability and efficiency of the substation's overall functionality [2]. This makes UHF partial discharge sensors indispensable components in modern substations. However, the operating environment of substations is highly complex, characterized by multiple physical and environmental stressors, such as electromagnetic interference, high humidity, extreme temperatures, and mechanical vibrations. These factors create a challenging scenario whereby UHF partial discharge sensors experience higher failure rates compared to other application environments [3]. The sensors not only need to withstand the stresses of the electrical and physical conditions within substations, but they must also operate reliably despite these challenges. This greater susceptibility to failure can result in performance degradation or complete sensor malfunction, which, if not diagnosed and addressed promptly, can lead to system-wide malfunctions and, in severe cases, cause the substation to shut down entirely [4, 5]. This makes the rapid and accurate diagnosis of UHF sensor health a critical focus for researchers and engineers, as the early detection of faults is essential to maintaining system stability and avoiding catastrophic failures [6]. To address these challenges, a variety of fault diagnosis techniques have been developed in the field of sensor fault detection, each tailored to the unique characteristics of different sensor types [7, 8]. These techniques can generally be categorized into model-based,knowledge-based, and data-based approaches. Among these, data-driven techniques, particularly those based on machine learning, have shown significant promise [9]. Neural networks and support vector machines (SVMs) are two such methods that have demonstrated strong performance in sensor fault classification tasks [10]. These approaches have been applied across various types of sensors, including pressure sensors and temperature sensors, yielding high accuracy in fault detection and diagnostic tasks [11]. The use of machine learning models has surpassed traditional manual fault detection methods, offering greater efficiency and precision [12]. For instance, [PERSON] et al. [13] proposed a Dense Convolutional Network (DenseNet) for reconstructing the real building response under environmental stimuli. Similarly, [PERSON] et al. [14] applied the theory of autoencoders to data compression and reconstruction. While convolutional neural networks (CNNs) have proven effective in fault diagnosis, they often suffer from certain limitations, such as long training times and decreased accuracy when dealing with small datasets [15]. As the field of data-driven fault diagnosis continues to evolve, significant innovations have been made, particularly in the realm of neural network architectures [16]. Starting with the introduction of AlexNet, and its revolutionary performance in the ImageNet image classification challenge, CNNs have rapidly evolved into more complex and powerful architectures, featuring larger scales and broader connectivity [17]. However, despite these advances, CNNs remain inherently limited by their reliance on local feature extraction, which can make it difficult for them to capture global features across an entire image [18]. On the other hand, the development of Transformer-based models, originally designed for natural language processing (NLP) tasks, has opened new avenues for sensor fault diagnosis.