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An efficient representative for object recognition in structural health monitoring

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Abstract

Structural health monitoring (SHM) program has been widely applied for damage assessment or monitoring the performances and the reliabilities of structures. Usually, structural damage is assessed by discriminating abnormal change from normal change in the structural dynamic behavior. This paper takes the SHM data from a real bridge as a study example and recognizes the trains which daily cross this bridge. The main aim of the current work is to improve the accuracy of structural damage detection. In this paper, symbolic data analysis (SDA) is introduced to extract hidden patterns from raw data, and principal component analysis (PCA) is performed to extract the most important properties of the signals and eliminate the influence of noise. A new representative is proposed in this paper and the recognition results obtained by the clustering algorithms with different representatives are compared. The results show that the new representative is robust to recognize the trains, and PCA is very useful to extract the properties of the signals as well as to reduce the influence of noise.

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Authors and Affiliations

  1. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, Shaanxi, 710048, China

    WeiChao Guo, HuaiShan Zhao, XinQin Gao, LingFei Kong & Yan Li

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  1. WeiChao Guo
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  2. HuaiShan Zhao
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  3. XinQin Gao
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  4. LingFei Kong
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  5. Yan Li
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Correspondence to WeiChao Guo.

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Guo, W., Zhao, H., Gao, X. et al. An efficient representative for object recognition in structural health monitoring. Int J Adv Manuf Technol 94, 3239–3250 (2018). https://doi.org/10.1007/s00170-016-9309-6

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  • DOI: https://doi.org/10.1007/s00170-016-9309-6

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