[22] described the performance degradation processes of turbine impellers by developing a physical model of their failures. [PERSON] et al. [23] and [PERSON] et al. [24] characterized the turbine impeller performance degradation law by the cumulative damage model and developed a corresponding fatigue life prediction model. [PERSON] et al. [25] established a nonlinear degradation model of a turbine impeller through neural network regression. [PERSON] et al. [26] used the Wiener process to establish a competitive failure model of an impeller to provide a basis for maintenance and reliability studies of the related components. Nonetheless, this research evaluates the product design life of a turbine impeller. In engineering practice, two types of tests are carried out in the process of turbine development (due to the limitation of the development cycle and cost): one is for the component-level structure strengthening assessment test of the turbine, and the other is a performance assessment test for the components installed on the whole machine. A turbine-related durability assessment has the following practical problems:  
Problem I is the matching problem of the component strengthening test and whole machine system test. According to the component-level structural strengthening test, for example, in China, the test for a supercharger for special vehicles, the Turbocharger 120-h Structural Assessment Test Syllabus, is a type of strengthening test. It is also the national military standard for a 350-h complete test of the whole performance assessment test. The reinforcement test is a qualitative assessment method used to find the weak link in a product by rapidly increasing the test stress, while finding a way to using the shortest time to efficiently stimulate failure. However, it is difficult to meet the quantitative verification of the durability level of the supercharger, and whether or not the supercharger 120-h structural assessment test meets the requirements of the 350-h complete test does not have a clear mathematical explanation. Problem II is the design problem of the two kinds of bench tests. The whole engine bench test time is no longer carried out according to the traditional method, such as when testing a special engine with a whole bench test from 350 h to 500 h of change, and how to provide an improved scientific basis for a supercharger structure assessment test has become an urgent problem. Therefore, for a special vehicle turbine, this paper aims to establish an accelerated degradation durability evaluation model to solve the above two types of problems. The contributions of this paper are as follows:  
(1) Based on GA-BP neural network for degradation data (bench test phase), we first propose a durability assessment model which can effectively avoid the problem of the complicated process of resolving multi-dimensional equation systems. Furthermore, the non-linear relationship between multiple stresses and the amount of performance degradation can be well mapped. (2) We propose an effective way to solve the above two types of problems, and it can be used to guide solutions to similar problems. The remaining structure of the article is as follows: Combined with the turbine-related bench test, the degradation parameters and sensitive parameters required by the accelerated degradation model are selected in Section 2. Section 3 proposes a turbine accelerated degradation durability assessment model based on a GA-BP neural network for assessing the turbine endurance limit. Section 4 discusses both the validity of the model and the application of the model, explaining the problems faced by the durability assessment as described above. Section 5 is the conclusion. 2 Characterization Parameters of the Turbine Impeller Degradation Failure Mechanism for a Bench Test