Multi-source Dynamic Loads Research Articles

Deep regression adaptation networks with model-based transfer learning for dynamic load identification in the frequency domain

Frequency-domain dynamic load identification methods based on neural network (NN) models construct models independently at each frequency, but are inaccurate and inefficient to train. To address these problems, a deep regression adaptation network (DRAN) with model-transfer learning is proposed for identifying dynamic loads in the frequency domain. The aim is to take advantage of the similarity of uncorrelated multi-source dynamic loads and multi-vibration response at adjacent frequencies. First, a DRAN model for load identification is established using the historical data for a specific frequency. Second, the trained DRAN parameters are transferred to the DRAN for the target frequency as the initial parameter values. Next, the transferred DRAN is fine-tuned with the historical data of the target frequency to obtain the load identification model of the target frequency. Finally, the trained DRAN parameters of the current target frequency are transferred to the next target frequency. This process is iterated until a DRAN model for all frequencies is established. Because a frequency response function is a continuous function varying with frequency, the relationships between the dynamic loads and response at adjacent frequencies are similar. DRAN can adapt the historical data of different frequencies to one neural network for training, and then extract the common feature information of different frequencies to improve the accuracy of the model. Moreover, instead of setting the initial weights randomly and training them independently for each DRAN model, model-transfer learning is used to obtain better initial weights from the trained weights of DRAN models of adjacent frequencies. The proposed method was evaluated on the experimental data of a cylindrical shell structure under acoustic vibration joint excitation. The results show that the proposed method can obtain better initial weights, higher accuracy, better noise robustness, and shorter training time than a neural network.

A new regularization method for dynamic load identification.

Dynamic forces are very important boundary conditions in practical engineering applications, such as structural strength analysis, health monitoring and fault diagnosis, and vibration isolation. Moreover, there are many applications in which we have found it very difficult to directly obtain the expected dynamic load which acts on a structure. Some traditional indirect inverse analysis techniques are developed for load identification by measured responses. These inverse problems about load identification mentioned above are complex and inherently ill-posed, while regularization methods can deal with this kind of problem. However, most of regularization methods are only limited to solve the pure mathematical numerical examples without application to practical engineering problems, and they should be improved to exclude jamming of noises in engineering. In order to solve these problems, a new regularization method is presented in this article to investigate the minimum of this minimization problem, and applied to reconstructing multi-source dynamic loads on the frame structure of hydrogenerator by its steady-state responses. Numerical simulations of the inverse analysis show that the proposed method is more effective and accurate than the famous Tikhonov regularization method. The proposed regularization method in this article is powerful in solving the dyanmic load identification problems.

A new conjugate gradient algorithm for solving dynamic load identification

In this paper, we propose a new conjugate gradient method which possesses the global convergence and apply it to solve inverse problems of the dynamic loads identification. Moreover, we strictly prove the stability and convergence of the proposed method. Two engineering numerical examples are presented to demonstrate the effectiveness and speediness of the present method which is superior to the Landweber iteration method. The results of numerical simulations indicate that the proposed method is stable and effective in solving the multi-source dynamic loads identification problems of practical engineering.

An Improved Iterative Tikhonov Regularization Method for Solving the Dynamic Load Identification Problem

The purpose of this study is to identify multi-source dynamic loads acting on the practical engineering structure. In this study, we propose a new iterative Tikhonov regularization method (MITR) based on a new functional operator, and strictly prove its stability and definition from the mathematical theory. This inverse technology has been validated by numerical benchmarks and engineering example involving dynamic load identification. Numerical simulations indicate that the proposed method reduces the number of iterations and also can obtain more efficient and numerically stable approximation of the true loads compared with the iterative Tikhonov regularization method (ITR), which verifies the robustness and effectiveness of the proposed method.

An efficient conjugate gradient method and application to dynamic force reconstruction

In this paper, we propose and create an efficient conjugate gradient method (MCG) based on a new operator, prove its stability and convergence and apply it to the multi-source dynamic loads identification of practical engineering structure. Numerical simulations of two engineering examples demonstrate that the present method has high efficiency and very robust convergence performance, and reduces the number of iterations. In addition, the proposed method can provide more efficient and numerically stable approximation of the true loads, compared with the Landweber iteration method. The results validate the stability and the effectiveness of the present method and coincide with the results of the theoretical analysis.

A NOVEL REGULARIZATION METHOD AND APPLICATION TO LOAD IDENTIFICATION OF COMPOSITE LAMINATED CYLINDRICAL SHELL

In this paper, a novel regularization method (MRO) is suggested to identify the multi-source dynamic loads on a surface of composite laminated cylindrical shell. Regularization methods can solve the di±culty of the solution of ill-conditioned inverse problems by the approximation of a family of neighbouring well-posed problems. Based on the construction of a new regularization operator, corresponding regularization method is established. We prove the stability of the proposed method according to suitable parameter choice strategy that leads to optimal convergence rate toward the minimalnorm and least square solution of an ill-posed linear operator equation in the presence of noisy data. Furthermore, numerical simulations show that the multi-source dynamic loads on a surface of composite laminated cylindrical shell are successfully identi¯ed, and demonstrate the e®ectiveness and robustness of the present method.

An Novel Computational Method and Application to Reconstruction of Dynamic Loads on a Plate

We propose an novel computational method for solving dynamic loads identification problems. The algorithm considered here is detailedly given. The present method is applied to the identification of the multi-source dynamic loads on a surface of plate. Numerical simulations validate the stability and effectivity of the proposed method.

A New Computational Inverse Method and Application to the Identification of Dynamic Loads

In this paper, we propose a new computational inverse method for solving the identification of multi-source dynamic loads acting on a simply supported plate. Using a priori choosing appropriate regularization parameter, the present method can obtain higher optimum asymptotic order of the regularized solution than ordinary Tikhonov regularization method. In the numerical simulations, the identification problem of multi-source dynamic loads on a surface of simply supported plate is successfully solved by the present method. Meanwhile, most of its performances are better than ordinary Tikhonov regularization method.

A new conjugate gradient method for solving multi-source dynamic load identification problem

In this paper, a new conjugate gradient method is suggested to estimate the multi-source dynamic loads acting on a surface of 3D plate. The algorithm reconstructs the distribution functions of the dynamic loads by the displacement responses at two receiving points. The dynamic Green’s function and response function of dynamic forces excitation are obtained by finite element method. Under linear and time-invariant supposition, the response at a receiving point on the structure can be expressed as a convolution integral of the desired unknown force and the corresponding Green’s function, where the continuous convolution functions are spatially and temporally discretized by trapezium formula. The preset method is suggested to identify the dynamic loads by minimizing the error functions. Finally, the multi-source dynamic loads are successfully identified by the proposed method, and the performance of numerical simulations is superior to Landweber iteration method, which demonstrates the efficiency and robustness of the proposed method.

A New Conjugate Gradient Method for the Solution of Linear Ill-Posed Problem

A new conjugate gradient method is proposed for solving the linear ill-posed problem and the application to the identification of the multi-source dynamic loads on a surface of simply supported plate. The algorithm considered here is detailedly given and proved that the computational costs for the present method are nearly the same as the common conjugate gradient method, but the number of iteration steps is even less. Finally, the performances of numerical simulations are given, and verify the favorable theoretical properties of the present method.

A New Homotopy Perturbation Method for Solving an Ill-Posed Problem of Multi-Source Dynamic Loads Reconstruction

A New Homotopy Perturbation Method for Solving an Ill-Posed Problem of Multi-Source Dynamic Loads Reconstruction

An improved iteration regularization method and application to reconstruction of dynamic loads on a plate

We present an improved iteration regularization method for solving linear inverse problems. The algorithm considered here is detailedly given and proved that the computational costs for the proposed method are nearly the same as the Landweber iteration method, yet the number of iteration steps by the present method is even less. Meanwhile, we obtain the optimum asymptotic convergence order of the regularized solution by choosing a posterior regularization parameter based on Morozov’s discrepancy principle, and the present method is applied to the identification of the multi-source dynamic loads on a surface of the plate. Numerical simulations of two examples demonstrate the effectiveness and robustness of the present method.

A new regularization method and application to dynamic load identification problems

In this article, a new regularization method is proposed and applied to the identification of multi-source dynamic loads acting on a surface of composite laminated cylindrical shell. In general terms, regularization is the approximation of an ill-posed problem by a family of neighbouring well-posed problems. Based on the construction of a new regularization operator, corresponding regularization method is established. We propose a regularization operator, thus construct a new regularization method, and prove its regular property. The present method is applied to load identification of composite laminated cylindrical shell. The transient displacement response can be obtained by the finite element method. The multi-source dynamic loads on a surface of composite laminated cylindrical shell are successfully identified, which demonstrates the efficiency and robustness of the present method.

A New Homotopy Perturbation Method for Solving an Ill-Posed Problem of Multi-Source Dynamic Loads Reconstruction

A New Homotopy Perturbation Method for Solving an Ill-Posed Problem of Multi-Source Dynamic Loads Reconstruction