大连海事大学学报 >
2018 , Vol. 44 >Issue 1: 91 - 98
DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2018.01.014
叶片前缘造型对压气机平面叶栅气动特性的影响
收稿日期: 2017-06-12
修回日期: 2017-07-18
网络出版日期: 2017-07-18
基金资助
国家自然科学基金资助项目(51479018;51379002); 中央高校基本科研业务费专项资金资助项目(3132016335).
Influence of blade leading edge design on aerodynamic characteristics of compressor plane cascade
Received date: 2017-06-12
Revised date: 2017-07-18
Online published: 2017-07-18
杨凌 , 李大春 , 钟兢军 , 李勇俊 . 叶片前缘造型对压气机平面叶栅气动特性的影响[J]. 大连海事大学学报, 2018 , 44(1) : 91 -98 . DOI: 10.16411/j.cnki.issn1006-7736.2018.01.014
In order to solve the train suffer and gradient extinction of recurrent neural network (RNN), a long-short term memory network (LSTM) algorithm was proposed and applied to the monitor of mechanical state (PMS) after introducing basic principle and designing updating algorithm of model. The simulation was carried out by taking the motor bearing data as samples, in view of the nonstationarity of bearing data, the empirical mode decomposition (EMD) was used to decompose the bearing data into stationary singles, and the intrinsic mode function (IMF) energy entropy was calculated as the feature of mechanical state. By comparing LSTM’s single-step prediction result of mechanical state with SVRM’s prediction result, it is shown that LSTM is better than support vector machine regression in terms of machine performance prediction.
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