Abstract: This paper briefly introduces Support Vector Regression (SVR) and constructs a sensor fault diagnosis system based on it. The SVR is trained offline and applied online to simulate the dynamic characteristics of a diesel engine temperature control sensor system. Simulation results show that the SVR can effectively simulate the dynamic characteristics of the sensor system, track the sensor output signal, and accurately diagnose sensor faults in a timely manner. Keywords: Support Vector Regression; Sensor; Fault Diagnosis 0 Introduction Sensors are sensitive components in measuring instruments, intelligent instruments, automatic control systems, and computer information input devices. The quality of sensors directly affects the operating status of equipment and is crucial to safety. In practical systems, due to the complex working environment, wide distribution, large data volume, and special installation location of sensors, they become one of the weakest links in process control and the most prone to failure. If a sensor fails during use, it will cause a decline in system performance and error accumulation, and may even lead to the paralysis of the entire system. Therefore, we hope that when a sensor fails, the fault can be diagnosed and isolated in a timely manner. To date, various methods for sensor fault diagnosis have been developed, such as hardware redundancy, analytical redundancy, Kalman filter, neural network observer group, and wavelet analysis [1]. However, these methods generally do not perform well. Support Vector Regression (SVR) is a new machine learning algorithm based on statistical learning theory [2]. It adopts the principle of structural risk minimization (SRM) and solves the problem of small sample learning well. It has a series of advantages such as globally unique optimal solution, strong generalization ability, and model structure automatically determined by the algorithm. This paper first briefly introduces the principle of SVR, then proposes a sensor fault diagnosis method and steps based on SVR, and finally verifies it with the output signal of the temperature sensor on a diesel engine condition monitoring system. For details, please click: A sensor system fault diagnosis method based on SVR