_applied_  
_sciences_  
_Article_  
**An Accelerated Degradation Durability Evaluation Model for the Turbine Impeller of a Turbine Based on a Genetic Algorithms Back-Propagation Neural Network**  
[PERSON] 1,2,*(), [PERSON] 1, [PERSON] 3, [PERSON] 4 and [PERSON] 3  
1 School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China  
2 Yangtte Delta Region Academy of Beijing Institute of Technology, Jiaxing 314003, China  
3 Quality and Reliability Center, China Institute of Marine Technology and Economy, Beijing 100081, China  
4 China North Engine Research Institute, Tianjin 300400, China  
* Correspondence: [EMAIL_ADDRESS]  
**Abstract: Durability evaluation plays an important role in product operation and maintenance during the design stage. In order to ensure a long life, high reliability, and short development cycle, an accelerated degradation durability evaluation model for the turbine impeller of a turbine based on a genetic algorithms back-propagation neural network is established. Based on the proposed model, we discuss two types of practical problems. One is the matching problem of the component strengthening test and whole machine system test. The other is the design problem of two kinds of bench tests. All in all, this work not only proposes a durability evaluation model to effectively solve the current turbine durability evaluation problems, but it also provides a feasible research idea for similar problems. **  
**Keywords: accelerated degradation model; durability evaluation; back-propagation neural network**  
**1. Introduction**  
A turbocharger is a key component of a special vehicle engine. If any part of a turbocharger fails, there will be serious consequences. Therefore, in the development stage, the durability assessment and evaluation of a turbocharger are crucial [1, 2]. Many studies have shown that a turbine impeller is an important key component and is an important link in determining a turbocharger's durability. At present, for the development of long-life and high-reliability products, it is difficult to obtain a durability test verification conclusion within the development cycle according to the traditional test method, which cannot meet the product development needs. Therefore, it is necessary to speed up the test process and shorten the test time. Until now, the current durability assessment has mainly been based on failure data and performance degradation. Assessment of the former is too dependent on failure data. In addition, the data is difficult to obtain due to the limited sample size and limited development cycle. To remedy the problem that traditional methods have--difficulty analyzing highly reliable products that lack failure data--scholars began to study the analysis methods based on performance degradation data, and it gradually become a hot topic [3]. As a study that analyzes methods based on performance degradation data, this research mainly focuses on the regression model, degradation distribution, and stochastic processes. The regression model uses regression fitting to obtain the product degradation trajectory through statistical analysis of the degradation data, and it mainly includes the failure physical model [4, 5, 6] and data fitting model [7, 8, 9]. The degradation distribution is used to determine the suitable degradation amount distribution function, such as the commonly used lognormal distribution [10] and Weibull distributions [11, 12, 13]. Based on these distribution functions, the parameter can be estimated and the degradation trajectory model can be determined. Furthermore, the stochastic process can describe the randomness of the degradation path caused by internal and external influences in the product degradation process, and the popular stochastic process models include the Wiener process [14; 15; 16] and Gamma process [17; 18; 19; 20]. In their studies on the durability evaluations of turbine impellers, [PERSON] et al. [21] and [PERSON] et al.