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Intelligent diagnosis of structural cracks with optimized sensing network

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Abstract

This paper considers intelligent diagnosis of structural cracks emanating from rows of rivet holes in thin metallic plates using active sensing network. Lamb waves are generated using actuators and propagate across the plates and received by sensors. We extract an effective feature called energy ratio change from time domain signals using wavelet transform. Then we develop neural networks using this feature to diagnose health condition. The sensing network is optimized by developing a mixed integer programming model. The results show that our method can effectively detect cracks and determine their locations, and the number of sensors of the sensing network can be significantly reduced while keeping high diagnostic accuracy. Important insights are also obtained such as in which area the sensing network has the weakest diagnostic capability.

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Authors and Affiliations

  1. A*STAR, Data Storage Institute (DSI), 5 Engineering Drive 1, Singapore, 117608, Singapore

    Shudong Liu, Chunling Du & Jingliang Zhang

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  1. Shudong Liu
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  2. Chunling Du
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  3. Jingliang Zhang
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Correspondence to Chunling Du.

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Liu, S., Du, C. & Zhang, J. Intelligent diagnosis of structural cracks with optimized sensing network. Microsyst Technol 19, 1653–1660 (2013). https://doi.org/10.1007/s00542-013-1875-z

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  • DOI: https://doi.org/10.1007/s00542-013-1875-z

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