Assessment research on damage degree of blade in aero-engine based on back-propagation artificial neural networks
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Assessment research on damage degree of blade in aero-engine based on back-propagation artificial neural networks
Vol. 26, Issue 4, (2011)
作者机构:
1. 空军工程大学 工程学院,西安,710038
2. 西安交通大学 机械工程学院 机械制造系统工程国家重点实验室,西安,710049
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Published:2011
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QIAN Zheng-wen, CHENG Li, ZHAO Bing-bing, et al. Assessment research on damage degree of blade in aero-engine based on back-propagation artificial neural networks[J]. 2011, 26(4).
DOI:
QIAN Zheng-wen, CHENG Li, ZHAO Bing-bing, et al. Assessment research on damage degree of blade in aero-engine based on back-propagation artificial neural networks[J]. 2011, 26(4).DOI:
Assessment research on damage degree of blade in aero-engine based on back-propagation artificial neural networks
natural frequency and the location of damage was deduced by simplifying the blade as a cantilever.In view of the problem in the maintenance of damaged blade
the relationship was abstracted into a mapping about damage degree
natural frequency and the location of damage.Then the neural network
which has the ability of self-study and can approach the nonlinear mapping
was used in the prediction of the damage.The damage degree assessment method of blade was built up based on the back-propagation(BP)neural network.Furthermore
this method was validated by using the experimental data of a certain aero-engine blade as the sample and test data.The results indicate that this method is useful to disposal of damaged blade in the project applications and can be applied in the maintenance criterion of blade.