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Compressive sampling and data fusion-based structural damage monitoring in wireless sensor network

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Abstract

The Lamb wave phased array structural health monitoring method is effective in structural damage monitoring. In this method, the damage scattering signal can be obtained by comparing the damage structural response signal with health structural response signal, and it can be used for structural damage identification. But in the structural health monitoring based on wireless sensor networks, this method has some inevitable defects in data transmission. A large number of sampling data of damage response signal will cause huge wireless communication burden. To solve this problem, we proposed a phased array image method based on compressive sampling and data fusion for wireless structural damage monitoring. First, compressive sampling signal by compressive sampling method was collected. Then, data fusion for multi-sensor’s damage response signal was implemented in phased array. Finally, the Lamb wave phased array damage identification method based on compressive sampling and data fusion was proposed. Experimental results on carbon composite structure show that the proposed method can largely save network bandwidth and energy. This method can also realize the damage identification accurately on the aviation aluminum plate and keep the detection error within 0.82 mm.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61672290, 61402234, 51505234, 51305211), Six talent peaks project in Jiangsu Province (XYDXXJS-040) and by the PAPD. Professor Jin Wang is the corresponding author.

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Authors and Affiliations

  1. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, China

    Sai Ji, Chang Tan, Ping Yang, Ya-Jie Sun & Desheng Fu

  2. School of Information Engineering, Yangzhou University, Yangzhou, China

    Jin Wang

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  1. Sai Ji
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  2. Chang Tan
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  3. Ping Yang
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  4. Ya-Jie Sun
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  5. Desheng Fu
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  6. Jin Wang
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Correspondence to Jin Wang.

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Ji, S., Tan, C., Yang, P. et al. Compressive sampling and data fusion-based structural damage monitoring in wireless sensor network. J Supercomput 74, 1108–1131 (2018). https://doi.org/10.1007/s11227-016-1938-x

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