Dissertation
Dissertation > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Artificial Neural Networks and Computing

Technology of Fault Diagnosis in Pile Based on Artificial Neural Network

Author LiNa
Tutor ZhaoXiaoAn
School Hebei University of Technology
Course Pattern Recognition and Intelligent Systems
Keywords pile artificial neural network fault diagnosis BP artificial neural network
CLC TP183
Type Master's thesis
Year 2007
Downloads 46
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The fault diagnosis in piles for building is a complex work which involves widely. It needs various knowledge and professional experience for the structural design. It has strong colligation, and contains lots of uncertain randomicity mistiness and unknown information. Because of the molding of pile finished underground. In traditional way the fault diagnosis is by seasoned expert.A way realized in this paper to fault diagnosis is using artificial neural network. The qualities of the artificial neural network, high-nonlinear, high-permissibility of error and high-robustness, self-adaptability, online work, and so on, are adequately used in the research. And this way overcomes the limitation. From the experiment result we know that the method based on artificial neural network can solve the problem of fault diagnosis in piles for building.At first, this paper researchs the present research conditions and related theories of fault diagnosis and artificial neural network, summarizes the theories of BP network. Then I mold the BP artificial neural network about fault diagnosis with the characteristic of pile. And during the experiment I give the solution to the disadvantage of BP network. Later realize the model in the Matlab 7.0.In the end to test the network with test sample. By testing, the improved BP network is better than the former in definition and veracity. The improved BP network is well and gets the goal of fault diagnosis. The result shows that this method has an important practical significance and a referential worth in the fault diagnosis in piles.

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