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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