Damage identification of an offshore wind turbine supporting structure based on a CNN-GRU parallel network&nbsp;

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (20) : 229-237.

Damage identification of an offshore wind turbine supporting structure based on a CNN-GRU parallel network

LI Xingjian,DIAO Yansong,L Jianda,HOU Jingru

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Abstract

When using vibration response and deep learning for structural damage identification, the problems such as requiring more data of measuring points, low accuracy of damage identification, and over-fitting of the network will be encountered. Therefore, a novel structural damage identification method based on Convolutional Neural Networks ( CNN ) -Gated Recurrent Unit ( GRU ) parallel neural network are presented in this paper. Firstly, the Generalized S Transform ( GST ) is performed on the measured response signal to obtain the GST time-frequency diagram. Then, CNN and GRU are used to extract time-frequency features and temporal features from time-frequency diagrams and response signals, respectively. The time-frequency features and temporal features are spliced and input into the fully connected layer and Softmax classifier for structural damage identification. The verification results of the model test data of offshore wind power supporting structure under displacement excitation show that the proposed method only needs the response signal of one measuring point and has higher identification accuracy and efficiency than other similar methods.

Key words

CNN-GRU parallel network / structural damage identification / deep learning / offshore wind power supporting structure / generalized S-transform

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LI Xingjian, DIAO Yansong, L Jianda, HOU Jingru. Damage identification of an offshore wind turbine supporting structure based on a CNN-GRU parallel network [J]. Journal of Vibration and Shock, 2024, 43(20): 229-237

References

[1] Purarjomandlangrudi A, Nourbakhsh G, Esmalifalak M, et al. Fault detection in wind turbine: a systematic literature review[J]. Wind engineering, 2013, 37(5): 535-547.
[2] Haselibozchaloee D, Correia J; Mendes P, et al. A review of fatigue damage assessment in offshore wind turbine support structure[J]. International Journal of Fatigue, 2022, Vol.164: 107145.
[3] Avci O, Abdeljaber O, Kiranyaz S, et al. A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications [J]. Mechanical Systems and Signal Processing, 2021, 147.
[4] Binbin Qiu, Yang Lu, Liping Sun, Xianqiang Qu, Yanzhuo Xue, Fushan Tong. Research on the damage prediction method of offshore wind turbine tower structure based on improved neural network. Measurement, 2020 ,151,107141.
[5] Bryan Puruncajas, Yolanda Vidal , Christian Tutivén. Vibration-Response-Only Structural Health Monitoring for Offshore Wind Turbine Jacket Foundations via Convolutional Neural Networks. Sensors 2020, 20, 3429; doi:10.3390/s20123429.
[6] Abdeljaber O, Avci O, Kiranyaz S , et al. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks[J]. Journal of Sound & Vibration, 2017, 388:154-170.
[7]Khodabandehlou H, Pekcan G, Fadali M S. Vibration‐based structural condition assessment using convolution neural networks[J]. Structural Control & Health Monitoring, 2019, 26(2):e2308.1-e2308.12.
[8]Sergio Cofre-Martel, Philip Kobrich , Enrique Lopez Droguett , Viviana Meruane. Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data. Shock and Vibration. 2019, Article ID 9859281, 27 pages. https://doi.org/10.1155/2019/9859281.
[9]Heng Liu , Yunfeng Zhang. Deep learning-based brace damage detection for concentrically braced frame structures under seismic. Advances in Structural Engineering,2019, Vol. 22(16) 3473–3486.
[10]Heng Liu, Yunfeng Zhang. Deep learning based crack damage detection technique for thin plate structures using guided lamb wave signals. Smart Materials and Structures 2020, 29 （1）： 015032 0964-1726.
[11] Tang Z, Chen Z, Bao Y, et al. Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring[J]. Structural Control & Health Monitoring, 2019, 26(1):e2296.1-e2296.22.
[12] Yang, J., Zhang, L., Chen, C., Li, Y., Li, R., Wang, G., Jiang, S., Zeng, Z., 2020. A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection. Information Sciences, 540, 117–130.
[13] Xingxian Bao, Tongxuan Fan, Chen Shi, Guanlan Yang. Deep learning methods for damage detection of jacket-type offshore platforms. Process Safety and Environmental Protection 154 (2021) 249–261.
[14] Tam T. Truong, Jaehong Lee, T. Nguyen-Thoi. An effective framework for real-time structural damage detection using one-dimensional convolutional gated recurrent unit neural network and high performance computing. Ocean Engineering 253 (2022) 111202
[15] Yang, J., Yang, F., Zhou, Y., Wang, D., Li, R., Wang, G., Chen, W., 2021. A data driven structural damage detection framework based on parallel convolutional neural network and bidirectional gated recurrent unit. Information Sciences,566, 103–117.
[16] J Z Zou, J X Yang, G P Wang, Y L Tang , C S Yu. Bridge structural damage identification based on parallel CNN-GRU. ACEER 2020, IOP Conf. Series: Earth and Environmental Science 626 (2021) 012017
[17] Dyke S J, Bernal D, Beck J, et al. Experimental phase II of the structural health monitoring benchmark problem [C] //Proceedings of the 16th ASCE engineering mechanics conference, 2003.
[18]LeCun Y, Boser B, Denker J S, et al. Backpropagation applied to handwritten zip code recognition[J]. Neural computation, 1989, 1(4): 541-551.
[19] Cho K, Van Merriënboer B, Bahdanau D, et al. On the properties of neural machine translation: Encoder-decoder approaches[J]. arXiv preprint arXiv:1409.1259, 2014.
[20] 高大鹏, 朱建刚. 滑动窗口时空深度置信网络行为识别[J]. 计算机工程与设计, 2018, 39(8): 2654-2659.
[21] Jonkman J, Butterfield S, Musial W, et al. Definition of a 5-MW reference wind turbine for offshore system development[R]. National Renewable Energy Lab. (NREL), Golden, CO (United States), 2009.
[22] 任义建. 运行状态下基于响应分析的风电支撑结构损伤检测研究[D]. 青岛: 青岛理工大学, 2023.
[23] 王秋潇. 基于模态响应与深度学习的结构损伤识别研究[D]. 青岛: 青岛理工大学, 2022.