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Applied Sciences
Volume 12
Issue 18
10.3390/app12189302
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Article

An Accelerated Degradation Durability Evaluation Model for the Turbine Impeller of a Turbine Based on a Genetic Algorithms Back-Propagation Neural Network

by
Xiaojian Yi
1,2,*,
Zhezhe Wang
1,
Shulin Liu
3,
Xinrong Hou
4 and
Qing Tang
3
1
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
2
Yangtze 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
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(18), 9302; https://doi.org/10.3390/app12189302
Submission received: 23 August 2022 / Revised: 10 September 2022 / Accepted: 13 September 2022 / Published: 16 September 2022
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Health Detection of Machinery)
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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 accelerated degradation model; durability evaluation; back-propagation neural network

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MDPI and ACS Style

Yi, X.; Wang, Z.; Liu, S.; Hou, X.; Tang, Q. An Accelerated Degradation Durability Evaluation Model for the Turbine Impeller of a Turbine Based on a Genetic Algorithms Back-Propagation Neural Network. Appl. Sci. 2022, 12, 9302. https://doi.org/10.3390/app12189302

AMA Style

Yi X, Wang Z, Liu S, Hou X, Tang Q. An Accelerated Degradation Durability Evaluation Model for the Turbine Impeller of a Turbine Based on a Genetic Algorithms Back-Propagation Neural Network. Applied Sciences. 2022; 12(18):9302. https://doi.org/10.3390/app12189302

Chicago/Turabian Style

Yi, Xiaojian, Zhezhe Wang, Shulin Liu, Xinrong Hou, and Qing Tang. 2022. "An Accelerated Degradation Durability Evaluation Model for the Turbine Impeller of a Turbine Based on a Genetic Algorithms Back-Propagation Neural Network" Applied Sciences 12, no. 18: 9302. https://doi.org/10.3390/app12189302

APA Style

Yi, X., Wang, Z., Liu, S., Hou, X., & Tang, Q. (2022). An Accelerated Degradation Durability Evaluation Model for the Turbine Impeller of a Turbine Based on a Genetic Algorithms Back-Propagation Neural Network. Applied Sciences, 12(18), 9302. https://doi.org/10.3390/app12189302

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