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.

For aerospace application of structural health monitoring (SHM) technology, the problem of reliable damage monitoring under time-varying conditions must be addressed and the SHM technology has to be fully validated on real aircraft structures under realistic load conditions on ground before it can reach the status of flight test. In this paper, the guided wave (GW) based SHM method is applied to a full-scale aircraft fatigue test which is one of the most similar test status to the flight test. To deal with the time-varying problem, a GW-Gaussian mixture model (GW-GMM) is proposed. The probability characteristic of GW features, which is introduced by time-varying conditions is modeled by GW-GMM. The weak cumulative variation trend of the crack propagation, which is mixed in time-varying influence can be tracked by the GW-GMM migration during on-line damage monitoring process. A best match based Kullback-Leibler divergence is proposed to measure the GW-GMM migration degree to reveal the crack propagation. The method is validated in the full-scale aircraft fatigue test. The validation results indicate that the reliable crack propagation monitoring of the left landing gear spar and the right wing panel under realistic load conditions are achieved.

With the increase in aging aircrafts, corrosion monitoring has attracted much attention in the structural health monitoring area. Multiple signal classification has been gradually applied to structural health monitoring area as a new promising method because of its ability of directional scanning and the potential to monitor multiple signal sources. However, applying multiple signal classification algorithm to monitor real damage still faces some challenges. First, the scattered Lamb waves obtained using a single actuator is relatively weak, making the signal-to-noise ratio of the scattered signals low and resulting in the low precision of multiple signal classification–based monitoring. Second, linear sensor array–based structural health monitoring methods have the problem of blind area at the angles close to 0° and 180°. To meet these challenges and target at providing monitoring ability of both the position and severity of the damage, a novel transmitter beamforming and weighted image fusion–based multiple signal classification algorithm is proposed using a dual array that consists of two linear sensor arrays to enhance the amplitude of scattered Lamb waves from corrosion, improve its signal-to-noise ratio and eliminate the blind area. The corrosion severity can be evaluated by analyzing the largest eigenvalue of signal covariance matrix developed using the multiple signal classification algorithm. The proposed transmitter beamforming and weighted image fusion–based multiple signal classification algorithm is verified on aluminum plates with real corrosion damages at five stages. Experimental results show that the proposed method can realize corrosion monitoring with a good precision even at the blind monitoring area.

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The growing use of composite materials on aircraft structures has attracted much attention for impact monitoring as a kind of structural health monitoring (SHM) method. Multiple signal classification (MUSIC)-based monitoring technology is a promising method because of its directional scanning ability and easy arrangement of the sensor array. However, for applications on real complex structures, some challenges still exist. The impact-induced elastic waves usually exhibit a wide-band performance, giving rise to the difficulty in obtaining the phase velocity directly. In addition, composite structures usually have obvious anisotropy, and the complex structural style of real aircrafts further enhances this performance, which greatly reduces the localization precision of the MUSIC-based method. To improve the MUSIC-based impact monitoring method, this paper first analyzes and demonstrates the influence of measurement precision of the phase velocity on the localization results of the MUSIC impact localization method. In order to improve the accuracy of the phase velocity measurement, a single frequency component extraction method is presented. Additionally, a single frequency component-based re-estimated MUSIC (SFCBR-MUSIC) algorithm is proposed to reduce the localization error caused by the anisotropy of the complex composite structure. The proposed method is verified on a real composite aircraft wing box, which has T-stiffeners and screw holes. Three typical categories of 41 impacts are monitored. Experimental results show that the SFCBR-MUSIC algorithm can localize impact on complex composite structures with an obviously improved accuracy.

Structural health monitoring (SHM) of aircraft composite structure is helpful to increase reliability and reduce maintenance costs. Due to the great effectiveness in distinguishing particular guided wave modes and identifying the propagation direction, the spatial-wavenumber filter technique has emerged as an interesting SHM topic. In this paper, a new scanning spatial-wavenumber filter (SSWF) based imaging method for multiple damages is proposed to conduct on-line monitoring of aircraft composite structures. Firstly, an on-line multi-damage SSWF is established, including the fundamental principle of SSWF for multiple damages based on a linear piezoelectric (PZT) sensor array, and a corresponding wavenumber-time imaging mechanism by using the multi-damage scattering signal. Secondly, through combining the on-line multi-damage SSWF and a PZT 2D cross-shaped array, an image-mapping method is proposed to conduct wavenumber synthesis and convert the two wavenumber-time images obtained by the PZT 2D cross-shaped array to an angle-distance image, from which the multiple damages can be directly recognized and located. In the experimental validation, both simulated multi-damage and real multi-damage introduced by repeated impacts are performed on a composite plate structure. The maximum localization error is less than 2 cm, which shows good performance of the multi-damage imaging method. Compared with the existing spatial-wavenumber filter based damage evaluation methods, the proposed method requires no more than the multi-damage scattering signal and can be performed without depending on any wavenumber modeling or measuring. Besides, this method locates multiple damages by imaging instead of the geometric method, which helps to improve the signal-to-noise ratio. Thus, it can be easily applied to on-line multi-damage monitoring of aircraft composite structures.

The effects of austempering on the microstructures and mechanical performances of cast high carbon silicon and manganese steel (HC-SMS) containing 1.0 wt.%C-2.5 wt.%Si-1.5 wt.%Mn-1.0 wt.%Cr-0.5 wt.%Cu were studied. The test results show a plate-like morphology of bainitic ferrite. Each plate of the ferrite is surrounded by a thin layer of retained austenite when the austempering temperature is low, whereas large blocky areas of retained austenite are observed when the temperature is higher. The amount of retained austenite in the bainitic structure increases with increasing isothermal quenching temperature. Austempering results in a significant improvement in the mechanical performances of HCSMS. The main effect of the austempering temperature on the mechanical performances is that hardness and strength are decreased and elongation, impact toughness and fracture toughness are increased with increasing temperature. Cast HCSMS has excellent comprehensive mechanical performance when austenized at 593K.