An Adaptive Operational Modal Analysis Method Using Encoder LSTM with Random Decrement Technique

Min Qin,Tianci Gao,Huaihai Chen,Ronghui Zheng,Haidong Shao

doi:10.1155/2021/7199888

Abstract

A new parameter identification method under non-white noise excitation using transformer encoder and long short-term memory networks (LSTMs) is proposed in the paper. In this work, the random decrement technique (RDT) processing of the data is equivalent to eliminating the noise of the raw data. In general, the addition of the gate in LSTM allows the network to selectively store data, which avoids gradient disappearance and gradient explosion to a certain extent. It is worthwhile mentioning that the encoder can learn the essence of data, which reduces the burden for the LSTM. More specifically, establish as simple LSTM structure as possible to learn the data of this essence to achieve the best training effect. Finally, the proposed method is used for simulation and experimental verification, and the results show that the method has the advantages of high recognition accuracy, strong anti-noise ability, and fast convergence rate. Specially, the results indicated appropriate accuracy proposed by deep learning combined with traditional method for parameter identification as well as proper performance of the proposed method.

Highlights

Operational modal analysis only needs to measure the vibration response data of the structure, and there is no need to measure the input excitation, which saves the measure cost
The encoder long short-term memory networks (LSTMs) was established by repeatedly training with the iteration steps as 100 and the learning rate as 0.001
It is generally known that signal to noise ratio (SNR) [31] is a common index to evaluate the strength of noise in a signal

Summary

Introduction

Operational modal analysis only needs to measure the vibration response data of the structure, and there is no need to measure the input excitation, which saves the measure cost. The research on structural modal parameter identification under non-white noise excitation is beneficial to the further development of structural dynamic analysis technology, so as to be better applied to engineering. Ibrahim extended the RDT method to the field of multichannel signals and formed Ibrahim time-domain method, which was successfully applied to modal parameter identification of spacecraft model structure [6]. RNNs have unique advantages in processing time series data, and the time-domain method for modal parameter identification based on RNNs has great development potential. Using the advantages of traditional methods and neural networks to establish a new method is worth studying For this purpose, an adaptive operational modal analysis method using encoder LSTM with RDT is proposed in this paper.

Background

The Proposed Method

Experimental Verification

Conclusion