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Lookup NU author(s): Professor Gui Yun TianORCiD
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A new method for stress testing based on the theory of Barkhausen noise has been introduced using changing feature values for monitoring stress and temperature. However, changes in temperature not only have an effect on the stress but also the MBN signal itself. In order to get the accurate stress value and eliminate the temperature effect, we proposed a data processing method for stress testing based on MBN. The study found that within the steel elastic range, the Barkhausen noise feature values, including mean value, RMS value, ring numbers, peak value and the ratio of envelope peak and full peak width at half of maximum amplitude decrease with increasing temperature, there is a fixed monotonic relationship which provides a theoretical basis for building the back propagation (BP) neural network model, with stress as the output value and temperature, mean value, RMS value, ring numbers, peak value and the ratio of peak and full width of half maximum as the input values. The MATLAB 7.8.0 neural network toolbox was used to model and simulate the neural network and samples used to validate the trained BP neural network. The results showed that the network had a high degree of accuracy and generalization ability, to get the values of stress. (C) 2013 Elsevier Ltd. All rights reserved.
Author(s): Wang P, Zhu L, Zhu QJ, Ji XL, Wang HT, Tian GY, Yao ET
Publication type: Article
Publication status: Published
Journal: NDT & E International
Print publication date: 20/01/2013
ISSN (print): 0963-8695
ISSN (electronic): 1879-1174
Publisher: Elsevier Ltd
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