Dynamic load identification based on deep convolution neural network

Abstract

The deep learning methods have been extensively studied in the field of dynamic load identification, due to their strong direct modeling ability between vibration response and external excitation. Dynamic load identification is a complicated inverse problem, which extremely relies on the solution of the structural model parameter. Nevertheless, the accurate computation of model parameters is always a challenge, small errors in model parameters will lead to inaccuracy of dynamic load identification. This brings various hardships to engineering applications. To achieve this problem, we propose a novel method based on a deep dilated convolution neural network (DCNN) for dynamic load identification, directly constructing the inverse model between vibration response and excitation, avoiding solving the model parameter. A dynamic load identification model, which contains two 1-D dilated convolution layers, one pooling layer, and two fully connected layers, is constructed to estimate the sinusoidal, impact, and random dynamic loads of a simply supported beam. We also appraise the anti-noise ability of the proposed method for load identification. Moreover, a vibration test is carried out to further evaluate this algorithm in the experimental aspect. Additionally, we analyze the comparison of the proposed method along with the Green kernel function method, and the dynamic system with uncertain model parameters is also analyzed. Besides, the operation of the convolution layer for response input is studied, and the applicability to different distributions of measurement points and the adaptability for frequency domain data are investigated. Ultimately, we find this method has a strong anti-noise ability due to its convolution layer, which can be regarded as a filter in dynamic load identification. Furthermore, the proposed method is of great practical for engineering applications owing to its satisfying applicability for systems with uncertain parameters, distributions of measurement points, and frequency data. All the results reveal the advantages of the identification method based on DCNN, consisting of good accuracy, reliability, and robustness. These results can be favorable for many applications.