Abstract
The determination of structural dynamic characteristics can be challenging, especially for complex cases. This can be a major impediment for dynamic load identification in many engineering applications. Hence, avoiding the need to find numerous solutions for structural dynamic characteristics can significantly simplify dynamic load identification. To achieve this, we rely on machine learning. The recent developments in machine learning have fundamentally changed the way we approach problems in numerous fields. Machine learning models can be more easily established to solve inverse problems compared to standard approaches. Here, we propose a novel method for dynamic load identification, exploiting deep learning. The proposed algorithm is a time-domain solution for beam structures based on the recurrent neural network theory and the long short-term memory. A deep learning model, which contains one bidirectional long short-term memory layer, one long short-term memory layer and two full connection layers, is constructed to identify the typical dynamic loads of a simply supported beam. The dynamic inverse model based on the proposed algorithm is then used to identify a sinusoidal, an impulsive and a random excitation. The accuracy, the robustness and the adaptability of the model are analyzed. Moreover, the effects of different architectures and hyperparameters on the identification results are evaluated. We show that the model can identify multi-points excitations well. Ultimately, the impact of the number and the position of the measuring points is discussed, and it is confirmed that the identification errors are not sensitive to the layout of the measuring points. All the presented results indicate the advantages of the proposed method, which can be beneficial for many applications.
Highlights
External excitation is the main source of basic dynamic data for many engineering applications, such as structural dynamic characteristics, vibration response analysis, health monitoring, vibration fatigue analysis and vibration fault diagnosis [1,2,3,4,5], among others
The results without noise and the absolute errors of identification result are shown in Figure 5, in which we show comparisons of deep recurrent neural network (RNN), multilayer perceptron (MLP) and the actual load, as well as the absolute errors of deep RNN and MLP
The results show that the error of the sinusoidal load identification based on RNN is smaller than that based on MLP
Summary
External excitation is the main source of basic dynamic data for many engineering applications, such as structural dynamic characteristics, vibration response analysis, health monitoring, vibration fatigue analysis and vibration fault diagnosis [1,2,3,4,5], among others. It is necessary to inverse the structural dynamic characteristics matrix, which is often ill conditioned This can result in a major impact on the accuracy, especially in noisy environments [7]. Given the features of dynamic load identification, it can be classified as a regression problem of deep learning Both a vibration response signal and an external excitation signal are considered to involve change over time. Bidirectional long short-term memory (BLSTM) is introduced, which can connect previous and future information in the time domain These variants of the RNN model are useful for multi-series prediction problems. Starting with RNN and LSTM, we establish a complete dynamic load identification system in order to apply this method to engineering practice, and to successfully identify common dynamic loads In this approach, sinusoidal, impulse and random excitations are identified on a supported beam. This is a significant advantage that can be beneficial if the proposed method is extended to other engineering applications
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