## 1 Introduction  
With the promotion of modern industrial intelligent manufacturing, the traditional artificial manufacturing industry has been turning to the development trend of modern automated production with robots as the main body. As a large and complex system, the body-in-white (BIW) welding robot is difficult to judge and detect because of the complexity of the control object and the variety of tasks it handles. Faults in the welding production process lead to the shutdown of the welding production line as well as untimely or imperfect equipment maintenance, etc., which directly affects the welding production efficiency and reduces the quality of the body-in-white. Due to the highly integrated, independent, and intelligent nature of body-in-white welding robots, it is very challenging to study their overall normal operation statuses. Once a part of the welding robot fails, the loss and damage caused are far greater than those for ordinary mechanical equipment; the failure may cause equipment downtime, causing economic losses for the enterprise, and personnel accidents, leading to more serious disasters. Therefore, it is meaningful to conduct fault diagnosis research on the robot [1]. However, the complexity, correlation, and uncertainty of the welding machine system make it very difficult to study the fault diagnosis of the welding machine. It has become a difficult problem to establish a fault diagnosis model for welding robots and to improve the diagnosis accuracy. The BIW-welding robot is an integrated system containing mechanical, electrical, control, and other complex system components. The work object is complex, and the processing tasks are heavy, resulting in a high probability of failure. As a result, the research on its fault diagnosis has also received increasing attention from experts and scholars at home and abroad. At present, there are mainly three types of fault diagnosis methods: analytical model-based methods [2], data-driven methods [3], and qualitative knowledge-based methods [4]. [PERSON] et al. [5] proposed an adaptive order tracking method with a recursive Kalman filter algorithm to implement fault diagnosis on induction motors. [PERSON] et al. [6] proposed an improved fuzzy hierarchical analysis method to realize the application of condenser fault diagnostics in power plants. The above analytical model can effectively and accurately determine the fault. However, the number of variables and influencing factors in the real environment makes it difficult to establish accurate mathematical and analytical expressions. [PERSON] et al. [7] proposed a method based on the maximum overlap discrete wavelet transform and artificial neural networks to realize the fault diagnosis of a distributed generation system; [PERSON] et al. [8] proposed a method combining feature vector selection and support vector machines to handle the question of classifying imbalanced data. [PERSON] et al. [9] proposed a hidden Markov model approach to achieve the application of ball mill gearbox fault diagnosis. The above methods have a powerful function for simulating the real system. However, in practice, a large amount of training data are often required to train the system model, and the training data often have the difficulty of a small sample size which is difficult to obtain. Most data-driven approaches are \"black-box modeling\", leading to problems such as the lack of interpretability of diagnostic results. [PERSON] et al. [10] proposed a VFDR fault diagnosis strategy based on expert knowledge to realize its application in variable refrigerant flow system fault diagnosis. [PERSON] et al. [11] proposed a fault tree and Bayesian-based approach to address applications in the fault diagnosis of overhead crane equipment. [PERSON] et al. [12] proposed a robot joint fault diagnosis method based on a BP neural network, which could solve the problem of the low accuracy of robot joint fault diagnosis. The above methods can obtain better fault diagnosis results.