基于贝叶斯正则化的多源/连续冲击载荷识别及试验研究

Here, aiming at ill-posed problems of ill-posed matrix inversion and noise sensitivity in impact load identification, an improved Bayesian method for augmented Tikhonov regularization technique was proposed. By introducing wavelet thresholding method to solve the problem of poor accuracy in identifying multi-source/continuous impact load under high noise level, the optimal regularization parameters were adaptively determined in identification process, and effects of noise on impact load identification were effectively eliminated. By performing numerical simulation analysis of aircraft wall panel structures under different impact loads and noise with different signal-to-noise ratios, taking correlation coefficients and relative errors as evaluation indexes, recognition effects obtained with Tikhonov regularization method based on L-curve method and generalized cross validation method, Bayesian regularization method and the proposed method, respectively were discussed contrastively. The results showed that the proposed method takes into account both curve smoothness and peak recognition accuracy, and the average peak error doesn' t exceed 14% when identifying continuous impact load at a high noise level of 20 dB. Impact tests were conducted for typical reinforced wall panel structures to verify the recognition ability of the proposed method for typical multi-source/continuous impact loads in practical projects. It was shown that the average peak error can be controlled within 18% ; the proposed method can provide an effective way for solving load recognition problems in engineering applications.