Journal of Dalian Maritime University >
2018 , Vol. 44 >Issue 1: 91 - 98
DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2018.01.014
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
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.
YANG Ling , LI Da-chun , ZHONG Jing-jun , LI Yong-jun . Influence of blade leading edge design on aerodynamic characteristics of compressor plane cascade[J]. Journal of Dalian Maritime University, 2018 , 44(1) : 91 -98 . DOI: 10.16411/j.cnki.issn1006-7736.2018.01.014
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