Tianxiang Huang

This paper studies a structural reliability analysis method based on adaptive neural network ensembles combined with Monte Carlo simulation (ANNE-MCS) during the water landing of wing-in-ground (WIG) aircraft where artificial neural networks are employed as the surrogate model. To guarantee the accuracy of the failure boundary fitting, a weighted mean square error loss function is constructed, which enhances the weights of samples located in proximity to the failure boundary. Consequently, the neural network model with locally accurate surrogate capability can be obtained with a minimum number of samples. Additionally, the proposed method combines with an active learning strategy, employing the U learning function to perform adaptive sampling in regions with high predictive variances and proximity to the failure boundary, thus reducing the number of required samples. Finally, the proposed method is applied to evaluate the reliability of the WIG aircraft belly structure under multiple water landing conditions, providing references for water landing operations. Fluid-structure interaction models are adopted to calculate the structural stress, considering pitch angles of 0°, 3°, 5°, and 7° at the landing speed of 24m/s. The results demonstrate that the error of the failure probability obtained by ANNE-MCS is only 0.5%-4% compared to the true values. A comparative analysis of the four cases reveals that the maximum structural stress values occur at the step in all cases. Furthermore, the failure probability of the WIG aircraft belly structure is found to be lowest when landing at a pitch angle of 7° in the defined step angle.

Inverse finite element-based shape reconstruction method for large-scale space antenna Tianxiang Huang, Tianyu Dong, Shenfang Yuan [button link="https://www

Guided Wave (GW)-based crack monitoring method as a promising method has been widely studied, as this method is sensitive to small cracks and can cover a wide monitoring range. Online crack quantification is difficult as the initiation and growth of crack are affected by various uncertainties. In addition, crack-sensitive GW features are influenced by time-varying conditions which further increase the difficulty in crack quantification. Considering these uncertainties, the Gaussian mixture model (GMM) is studied to model the probability distribution of GW features. To further improve the accuracy and stability of crack quantification under uncertainties, this paper proposes a multi-dimensional uniform initialization GMM. First, the multi-channel GW features are integrated to increase the accuracy of crack quantification, as GW features from different channels have different sensitivity to cracks. Then, the uniform initialization method is adopted to provide more stable initial parameters in the expectation-maximization algorithm. In addition, the relationship between the probability migration index of GMMs and crack length is calibrated with fatigue tests on prior specimens. Finally, the proposed method is applied for online crack quantification on the notched specimen of an aircraft spar with complex fan-shaped cracks under uncertainty.

One important topic for structural health monitoring is to achieve accurate damage detection with a small number of noisy sensors and without the requirement of a high-fidelity finite element model. This article adopts the Bayesian probabilistic approach combined with a perturbation model using responses at a few vibration nodes for damage monitoring. First, the node displacement, or the response at vibration node, is adopted in this study for real-time damage assessment with a relatively small number of sensors. Then, the construction method of the node displacement response curves based on the perturbation model is proposed to replace the expensive finite element model. After that, a Bayesian framework integrated the node displacement measurement and the response curves are adopted to acquire the probability distribution of the damage parameter. In this article, the accuracy of the node displacement–based Bayesian framework with the perturbation method is evaluated. The proposed method is applied to a supporting structure of a sailplane under different noise levels.

The electricity theft behavior is becoming more and more common in order to reduce the cost of consumption. A detection method for electricity theft based on random forest is proposed in this paper. Besides, the evaluation index system including the power consumption slope index, the line-loss index, and the warning information index is built to consider the factors affecting the electricity theft. For improving the detection accuracy, the data cleaning and miss value preprocess are conducted before training the decision trees. Finally, a numerical case and the comparison in performance with decision tree and GBDT are used to verify the availability and superiority of the proposed detection method.

Structural health monitoring (SHM) techniques, which are also considered as online nondestructive testing methods, are significant in modern structural engineering due to their ability to guarantee structure safety while reducing maintenance cost. It is often necessary to combine different SHM methods to achieve a more reliable damage detection result. However, the hardware of the SHM systems is usually expensive, bulky, and heavy when they are designed separately. Therefore, this paper proposes a three-layer architecture for designing an integrated multi-function SHM system to achieve a small, lightweight, and low power consumption SHM system. Based on the architecture, an integrated SHM system with impact monitoring and electromechanical impedance measurement is developed. In addition, a scheduling module is developed to manage the two functions of the system. Furthermore, an integrated interface is developed to transfer the data and the command. Then, an integrated printed circuit board is designed and manufactured to achieve the aforementioned functions. The designed system is applied for impact monitoring and damage detection for a supporting structure of a sailplane.