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: firstname.lastname@example.org
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
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