## 1 Introduction  
With the rapid development of the world's economy and the continuous improvement in people's living standards, the electricity load in urban and rural areas has also increased. This has brought more and more applications for dry-type transformers. Whether the transformer works normally or not directly affects the entire power system. The insulation performance of dry-type transformers is closely related to their heat generation and heat dissipation performance. If the winding temperature is too high, it will cause accelerated aging of the insulating material and shorten the service life of the transformer. This will result in its economic benefits not being maximized [1]. In extreme cases, it may even cause serious accidents such as transformer fires and explosions [2]. This will not only cause equipment damage, power outages in local areas, and cause massive economic losses to the society, but may also threaten the personal safety of the relevant personnel. Therefore, it is necessary to monitor the temperature status of the transformer in real-time when the transformer is working, and to respond to the abnormality in time [3; 4; 5]. However, the sensor used in the existing dry-type transformer temperature detection equipment hardly performs any self-confirmation on its working state, that is, the sensor is always considered to be working normally. In this way, once the sensor fails, its output results will seriously deviate from reality, which may cause false alarms, affect the work of the system, and even cause catastrophic consequences [6]. In sensor fault diagnosis, the difference between the signal of the model system and that of the actual system is defined as \"the residual\". Dynamic process models are applied to analyze the input and output signals. This method was first proposed by [PERSON] [7], but they did not give the details of the processing. There is abundant fault information in the residual signal, and the fault can be diagnosed based on an appropriate decision function or decision rule. The method is often used together with the fault estimation method to construct the sensor fault diagnosis process. By establishing a model and choosing a decision function (or rule), the evaluation function of the residual can be compared with the selected threshold function. As a result, a change is detected and a failure of the sensor system is judged [8]. [PERSON] et al. effectively detected the initial fault of the sensor in the high-speed railway electric drive device through SPE statistics [9]. [PERSON] et al. also used SPE statistics to diagnose the faults of sensors in the actual nuclear power plant facility system to reduce the system's false alarm rate [10]. [PERSON] et al. studied the fault detection and isolation of the electric-drive sensor based by improving the parity space method. A fast and simple algorithm for sensor fault detection was designed for second-order systems. The simplicity of the final algorithm led to a shorter execution time and less resource consumption in the conduction [11]. [PERSON] et al. proposed a sensor fault detection method based on nonlinear parity technology, which can be used in a pH neutralization system. The nonlinear fault detection and recognition algorithm can effectively detect and isolate the sensor fault on the pH channel as well as immediately and accurately detect the time of the fault occurrence [12]. In sensor data recovery, [PERSON] et al. proposed a pressure sensor data recovery model based on the correlation vector machine using the normal output data before the fault [13]. [PERSON] et al. proposed a recovery method for aircraft engine sensor failure based on the least squares support vector machine (LS-SVM) [14]. [PERSON], [PERSON] et al. proposed a structure response recovery method based on the convolutional neural network. Using the strain monitoring data stably measured before data loss, a convolutional neural network (CNN) model for data recovery was constructed.