Modern structures on aircraft make increasing use of large-scale composite structures. Quantitative damage monitoring for composites, including damage occurrence, number, localization, and size estimation, will help reduce maintenance costs, improve fleet management efficiency through condition-based maintenance, and potentially more rapidly enable new material systems and structural concepts by integrating health monitoring into the design itself. With the advantage of easily interpretable, intuitive, and accurate imaging result, the delay-and-sum imaging algorithm is frequently researched and applied to damage monitoring. However, when it is applied to multidamage monitoring in large-scale composites, the consumed time and computation resource are too much for pixel value calculation. Besides, due to the material anisotropy and existence of bolt holes and stiffeners in the researched complex carbon fiber composite laminate, propagation mechanism of Lamb wave is quite complicated, which makes the conventional localization result not accurate by delay-and-sum algorithm according to the point with pixel peak value. What is more, the imaging quality is deteriorated with concurrence of multiple damages, and thus, quantitative damage information cannot be extracted. Hence, the damage index merging algorithm is introduced for quick damage identification and damage merging in every subarea divided by piezoelectric sensor array. The delay-and-sum imaging algorithm is performed afterward only in subareas identified with damages, which significantly improves the efficiency of damage imaging for large-scale composites. The nonlinear normalization of pixel values compensates the deterioration of multidamage imaging quality. Then, a weighted average algorithm is introduced for more accurate localization. A further damage size level estimation is realized with probabilities by extracting the image pixel peak value. Experiments with six damages simultaneously on the complex carbon fiber laminate verify the effectiveness of the proposed quantitative multidamage monitoring algorithm with localization error below 2 cm and correct damage number and size level estimation.

The growing use of composite structures in aerospace structures has attracted much interest in structural health monitoring (SHM) for the localization of impact positions due to their poor impact resistance properties. The propagation mechanism and the frequency dispersion features of signals on complex composite structures are more complicated than those on simple composite plates. In this paper, a time reversal focusing based impact imaging method for impact localization of complex composite structures is proposed. A complex Shannon wavelet transform is adopted to extract frequency narrow-band signals of impact response signals of a PZT sensors array at a special time-frequency scale and to measure the phase velocity of the signals. The frequency narrow-band signals are synthesized using software, depending on the time reversal focusing principle, to generate an impact image to estimate the impact position. A demonstration system is built on a composite panel with many bolt holes and stiffeners on an aircraft wing box to validate this method. The validating results show that the method can estimate the position of impact efficiently.

Structural health monitoring technology for aerospace structures has gradually turned from fundamental research to practical implementations. However, real aerospace structures work under time-varying conditions that introduce uncertainties to signal features that are extracted from sensor signals, giving rise to difficulty in reliably evaluating the damage. This paper proposes an online updating Gaussian Mixture Model (GMM)-based damage evaluation method to improve damage evaluation reliability under time-varying conditions. In this method, Lamb-wave-signal variation indexes and principle component analysis (PCA) are adopted to obtain the signal features. A baseline GMM is constructed on the signal features acquired under time-varying conditions when the structure is in a healthy state. By adopting the online updating mechanism based on a moving feature sample set and inner probability structural reconstruction, the probability structures of the GMM can be updated over time with new monitoring signal features to track the damage progress online continuously under time-varying conditions. This method can be implemented without any physical model of damage or structure. A real aircraft wing spar, which is an important load-bearing structure of an aircraft, is adopted to validate the proposed method. The validation results show that the method is effective for edge crack growth monitoring of the wing spar bolts holes under the time-varying changes in the tightness degree of the bolts.

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Harmonious developments of electrical and mechanical performances are crucial for stretchable sensors in structural health monitoring (SHM) of flexible aircraft such as aerostats and morphing aircrafts. In this study, we prepared a highly durable ternary conductive nanocomposite made of polydimethylsiloxane (PDMS), carbon black (CB) and multi-walled carbon nanotubes (MWCNTs) to fabricate stretchable strain sensors. The nanocomposite has excellent electrical and mechanical properties by intensively optimizing the weight percentage of conducting fillers as well as the ratio of PDMS pre-polymer and curing agent. It was found that the nanocomposite with homogeneous hybrid filler of 1.75 wt % CB and 3 wt % MWCNTs exhibits a highly strain sensitive characteristics of good linearity, high gauge factor (GF ~ 12.25) and excellent durability over 105 stretching-releasing cycles under a tensile strain up to 25% when the PDMS was prepared at the ratio of 12.5:1. A strain measurement of crack detection for the aerostats surface was also employed, demonstrating a great potential of such ternary nanocomposite used as stretchable strain sensor in SHM.