A data fusion based diagnostic methodology for in-situ debonding detection in beam-like honeycomb sandwich structures with fiber Bragg grating sensors. (15th March 2022) - Yin, Jieming; Wang, Zechao; Liao, Wenlin; Hong, Liu; Ding, Yangyang; Zhou, Zude
Highlights: Theoretical model studies influences of debonding defect in honeycomb sandwich beam. Modified OMA extracts modal properties with a reduction in computational burden. Novel damage index based on D–S theory has good noise tolerance in diagnosis. FBG sensing array measures dynamic strain responses for facilitation of wiring layout. Experiments with seeded debonding validate performance of proposed diagnostic scheme. 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.