MATEC Web Conf.
Volume 232, 20182018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|Number of page(s)||5|
|Section||Network Security System, Neural Network and Data Information|
|Published online||19 November 2018|
Application of GA-LM-BP Neural Network in Fault Prediction of Drying Furnace Equipment
School of Mechanical Engineering, Hubei University of Technology, China
* Corresponding author: email@example.com
In the foundry, the surface dry furnace is a special equipment for surface drying after sand core hydrophobic coating. In order to accurately predict whether it was possible to malfunction, four objective variables was used as input, and the health status of the equipment was used as the output. A prediction model based on the traditional BP neural network was established. This model combined genetic algorithm (GA) to optimize the initial weight of BP neural network; combined with LM (Levenberg-Marquardt) algorithm to improve the BP neural network, the error decreased too slowly when the predicted value approached the target value. Four kinds of evaluation methods were used in Matlab to compare the prediction results of the three models in simulation training. The research shows that the improved algorithm can overcome the problem that the traditional BP neural network has slow convergence rate and is easy to fall into the local optimal solution, and it has higher prediction accuracy, which provides a new solution to the fault prediction of the surface dry furnace.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.