Title :
Vibration load identification based on the neural network model
Author_Institution :
Sch. of Mech. & Electron. Eng., Weifang Univ., Weifang, China
Abstract :
The paper utilizes the neural network method to identify the loads of the WZ12-1 offshore platform. Firstly, the finite element model of the WZ12-1 platform is build using the software of ANSYS. By applying loads to the identification points of the finite element model, we can obtain the data for training a neural network model. Then, a three-layer BP network is established and trained until converges using the data obtained previously. Finally, we input the actual response data into the trained network and obtain the corresponding loads on the WZ12-1 platform. The results show that the neural network method could gain a great advantage over the traditional techniques in the load identification of offshore platforms which are large and complex.
Keywords :
backpropagation; finite element analysis; mechanical engineering computing; neural nets; offshore installations; petroleum industry; vibrations; WZ12-1 offshore platform; backpropagation neural nets; finite element model; neural network model; three-layer BP network; vibration load identification; Artificial neural networks; Biological system modeling; Data models; Finite element methods; Force; Load modeling; Vibrations; WZ12–1; finite element model; load identification; neural network;
Conference_Titel :
Image and Signal Processing (CISP), 2010 3rd International Congress on
Conference_Location :
Yantai
Print_ISBN :
978-1-4244-6513-2
DOI :
10.1109/CISP.2010.5647303