Abstract
To adaptively identify the modal parameters for time-invariant structures excited by non-white noise, this paper proposes a new operational modal analysis (OMA) method using hybrid neural networks. In this work, taking the acceleration response directly as the input data of the networks not only simplifies the data processing, but also retains all the characteristics of the data. The data processed by the output function is the output data of the network, and its peak corresponds to the modal frequency. The proposed output function greatly reduces the computational cost. In addition, a small sample dataset ensures that the hybrid neural networks identify the modal parameters with the highest accuracy in the shortest possible time. Interestingly, the hybrid neural networks combine the advantages of the convolutional neural network (CNN) and gate recurrent unit (GRU). To illustrate the advantages of the proposed method, the cantilever beam and the rudder surface excited by white and non-white noise are taken as examples for experimental verification. The results reveal that the proposed method has a strong anti-noise ability and high recognition accuracy, and is not limited by ambient excitation type.
Highlights
operational modal analysis (OMA) is a process of identifying structural modal parameters only according to the response, including time-domain or frequency-domain techniques
The frequency-domain decomposition technique that has been widely studied by researchers [12–15] is constantly being improved; for instance, the enhanced frequency-domain decomposition (EFDD) algorithm can predict the natural frequencies, and estimate modal damping ratios [16]
The network output is consistent with the target output and the proposed method has no modal leakage, showing that the network has high recognition accuracy
Summary
OMA is a process of identifying structural modal parameters only according to the response, including time-domain or frequency-domain techniques. Various methods for identifying modal parameters of time-invariant structures were proposed [7–10]. In addition to the approaches above, the modal parameters of structures can be identified in another way by a neural network. [37], the improved fault diagnosis method based on CNN is proposed, which used a light neural network to process the original signal. The above methods and their application in practical engineering owing to the ability of the neural networks extract signal characteristics. The large amount of sample data increases the characteristic parameters of the model, resulting in a long training time, with an unstable model training effect To handle these concerns, a hybrid neural network based on CNN and GRU is proposed.
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