Gao Lihong
School of Mechanical Electronic Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
Xu Gening
School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China
Yang Ping
School of Mechanical Electronic Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
Yao Qiang
Shanxi Northern machinery manufacturing Co., LTD, Taiyuan, 030009, China
Zhang Min
Shanxi Northern machinery manufacturing Co., LTD, Taiyuan, 030009, China
ABSTRACT
A large amount of structural distribution data are required in the structure reliability analysis which usually obtained by the simulation or real test. But in fact large machine structure with low fault rate is often unable to get necessary statistic data. And the numerical simulation is very enormous for human and time consumption. Taking the advantages of the real test and simulation, the simulation is done by the Wavelet Neural Network (WNN) and in the real test the arm frame crane is looked as an object. Through the simulation the more samples are obtained to perfect the real test data. Applying to the stress statistical analysis, the results show that the measured stress data as samples to train WNN can further ensure actual prediction. And it is very high efficiency to predict the loading distribution data using the trained WNN. Its results can also meet the requirements of the project. And then the fatigue reliability of structure is computered using the probability distribution and the stress data. The application to the structure shows that the results can reflect the actual conditions.
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How to cite this article
Gao Lihong, Xu Gening, Yang Ping, Yao Qiang and Zhang Min, 2013. Study on the Stress Statistics and its Reliability of the Structure Based on WNN. Information Technology Journal, 12: 7101-7104.
DOI: 10.3923/itj.2013.7101.7104
URL: https://scialert.net/abstract/?doi=itj.2013.7101.7104
DOI: 10.3923/itj.2013.7101.7104
URL: https://scialert.net/abstract/?doi=itj.2013.7101.7104
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