A data fusion based diagnostic methodology for in-situ debonding detection in beam-like honeycomb sandwich structures with fiber Bragg grating sensors

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Abstract

The integrity of interfacial bonding in honeycomb sandwich should be diagnosed on regular basis. This paper develops a novel approach to diagnose debonding defects only employing strain measurements under ambient excitations. As debonding defects vary dynamics of the structure, the change ratio of strain modes is promising to indicate the defects. To address diagnostic errors caused by noises, a novel damage index is proposed based on Dempster–Shafer evidence theory, which works out a reasonable global decision from different orders of noisy strain mode shapes. Those modes are estimated by operational modal analysis motivating by the concept of rational fraction polynomial and transmissibility, which reduces the number of input condition and output fitting to single one. Fiber Bragg gratings are employed to capture the structural responses that can deploy as dense sensing nodes. As proof-of-concept testing, the proposed methodology is applied to a honeycomb sandwich beam with seeded debonding defects.

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Yin, J., Wang, Z., Liao, W., Hong, L., Ding, Y., & Zhou, Z. (2022). A data fusion based diagnostic methodology for in-situ debonding detection in beam-like honeycomb sandwich structures with fiber Bragg grating sensors. Measurement: Journal of the International Measurement Confederation, 191. https://doi.org/10.1016/j.measurement.2022.110810

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