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Global-Based Structure Damage Detection Using LVQ Neural Network and Bispectrum Analysis

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3498))

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Abstract

In this paper, a new approach for the global-based structure damage detection is proposed, which is based on the combination of bispectrum feature extraction technique and LVQ neural network identification method. A finite element model based on a steel frame structure with various joint damage patterns is analyzed. Results of analysis demonstrate higher damage identification capability in comparison with modal assurance criterion (MAC) method in a noise-contaminated environment.

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References

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Author information

Authors and Affiliations

  1. State Key Laboratory of Vibration, Shock& Noise, Shanghai Jiaotong University, Shanghai, 200240, China

    Guangming Dong, Jin Chen & Xuanyang Lei

  2. Institute of Structural Mechanics, Chinese Academy of Engineering Physics, Mianyang, Sichuan, 621900, China

    Zuogui Ning, Dongsheng Wang & Xiongxiang Wang

Authors
  1. Guangming Dong
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  2. Jin Chen
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  3. Xuanyang Lei
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  4. Zuogui Ning
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  5. Dongsheng Wang
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  6. Xiongxiang Wang
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Dong, G., Chen, J., Lei, X., Ning, Z., Wang, D., Wang, X. (2005). Global-Based Structure Damage Detection Using LVQ Neural Network and Bispectrum Analysis. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_85

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  • DOI: https://doi.org/10.1007/11427469_85

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25914-5

  • Online ISBN: 978-3-540-32069-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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