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【答案】应助回帖
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朱风立: 金币+5, ★★★★★最佳答案 2013-11-21 09:42:18
Accession number: 20134116843817
Title: A fault diagnosis method based on combination of neural network and fault dictionary
Authors: Meng, Ya Feng1 ; Zhu, Sai1 ; Han, Rong Li2
Author affiliation: 1 Electronic and Optical Engineering Department, Shijiazhuang Mechanical Engineering College, Shijiazhuang, China
2 Training Ministry, Shijiazhuang Mechanical Engineering College, Shijiazhuang, China
Source title: Advanced Materials Research
Abbreviated source title: Adv. Mater. Res.
Volume: 765-767
Monograph title: Advanced Information and Computer Technology in Engineering and Manufacturing, Environmental Engineering
Issue date: 2013
Publication year: 2013
Pages: 2078-2081
Language: English
ISSN: 10226680
ISBN-13: 9783037857984
Document type: Conference article (CA)
Conference name: 2013 International Conference on Advances in Materials Science and Manufacturing Technology, AMSMT 2013
Conference date: May 18, 2013 - May 19, 2013
Conference location: Xiamen, Fujian, China
Conference code: 99913
Publisher: Trans Tech Publications Ltd, Kreuzstrasse 10, Zurich-Durnten, CH-8635, Switzerland
Abstract: Neural network and Fault dictionary are two kinds of very useful fault diagnosis method. But for large scale and complex circuits, the fault dictionary is huge, and the speed of fault searching affects the efficiency of real-time diagnosing. When the fault samples are few, it is difficulty to train the neural network, and the trained neural network can not diagnose the entire faults. In this paper, a new fault diagnosis method based on combination of neural network and fault dictionary is introduced. The fault dictionary with large scale is divided into several son fault dictionary with smaller scale, and the search index of the son dictionary is organized with the neural networks trained with the son fault dictionary. The complexity of training neural network is reduced, and this method using the neural network's ability that could accurately describe the relation between input data and corresponding goal organizes the index in a multilayer binary tree with many neural networks. Through this index, the seeking scope is reduced greatly, the searching speed is raised, and the efficiency of real-time diagnosing is improved. At last, the validity of the method is proved by the experimental results. © (2013) Trans Tech Publications, Switzerland.
Number of references: 6
Main heading: Neural networks
Controlled terms: Binary trees
Uncontrolled terms: Combination of neural-network - Complex circuits - Fault diagnosis method - Fault dictionary - Fault sample - Index - Searching speed - Trained neural networks
Classification code: 723.4 Artificial Intelligence - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory
DOI: 10.4028/www.scientific.net/AMR.765-767.2078
Database: Compendex
Compilation and indexing terms, © 2013 Elsevier Inc. |
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