An Accelerated Degradation Durability Evaluation Model for the Turbine Impeller of a Turbine Based on a Genetic Algorithms Back-Propagation Neural Network
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Engineering Diagnostics and Reliability · Advanced Sensor Technologies Research · Reliability and Maintenance Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314003, China
Quality and Reliability Center, China Institute of Marine Technology and Economy, Beijing 100081, China
China North Engine Research Institute, Tianjin 300400, China
References (28)
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