Damage identification of transmission tower based on BO-BiLSTM model

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (1) : 238-248.

WEI Jiaheng, GUO Huiyong

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Abstract

The structure’s acceleration response sequence reflects the original structural state information and contains many damage characteristics. In response to the problem that the current transmission tower health monitoring system generates a large amount of data and cannot effectively analyze and diagnose the damage of the transmission tower, based on the time series relationship of the the structure’s output acceleration response data, this paper proposes a damage identification method based on a Bi-directional Long and Short-Term Memory (BiLSTM) network, and adopts a probability optimization method Bayesian optimization(BO) to determine the neural network hyperparameters. Firstly, this article describes the related theories of BiLSTM.Then it proposes a hyperparameter selection strategy based on Bayesian optimization and a damage identification method based on the BO-BiLSTM model. Then the method is applied to the finite element model of the transmission tower for damage localization and pattern identification, and the overall identification accuracy rate of test set reaches 94.2%. Finally, in order to verify method on the actual structure, a damage identification method based on heterogeneous data is proposed: the transmission tower finite element model data is used as the model training sample to train the BO-BiLSTM model, and the experimental data is used as the test set to test the damage recognition effect . The results show that the BO-BiLSTM can identify the real structure damage accurately, and the recognition effect is more stable than BiLSTM and BO-LSTM.

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damage identification / transmission tower / deep learning / BiLSTM / Bayesian optimization

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WEI Jiaheng, GUO Huiyong. Damage identification of transmission tower based on BO-BiLSTM model[J]. Journal of Vibration and Shock, 2023, 42(1): 238-248

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