Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades
Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades
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DOI：
10.1016/j.renene.2021.04.025
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发表时间：
2021-04-30
期刊：
影响因子：
8.7
通讯作者：
Kim, Moo-Hyun
中科院分区：
文献类型：
--
作者：
Choe, Do-Eun;Kim, Hyoung-Chul;Kim, Moo-Hyun
本文提出并测试了一种基于序列的深度学习 (DL) 模型，用于使用长短期记忆 (LSTM) 和门控循环单元 (GRU) 神经网络来检测浮动海上风力涡轮机 (FOWT) 叶片的结构损伤。完整的框架是通过使用 LSTM 和 GRU 网络的单向或双向层的四种不同的深度网络设计开发的。这些神经网络是专门为学习时序信息（例如时间序列数据）中的长期和短期依赖性而开发的，并成功地使用受损 FOWT 的传感器信号进行了训练。由于受损 FOWT 的现场数据可用性有限，因此使用先前通过实验测试验证的多种计算方法对传感器数据进行了模拟。模拟考虑了不同强度、位置和损伤形状的损伤场景，总共 1320 个损伤场景。在测试独立信号时，使用所选网络的最佳性能模型检测损坏的存在及其位置，准确度高达 94.8%。所选网络的 K 折交叉验证准确率估计为 91.7%。无论损坏位置如何，基于交叉验证，损坏本身的存在检测准确率为 99.9%。使用深度学习的结构损伤检测不受系统假设或环境条件的限制，因为网络直接从数据中学习系统。该框架可应用于各种类型的土木和海上结构。此外，基于序列的建模使工程师能够利用大量数字信息来提高结构的安全性。由爱思唯尔有限公司出版。
This paper proposes and tests a sequence-based modeling of deep learning (DL) for structural damage detection of floating offshore wind turbine (FOWT) blades using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks. The complete framework was developed with four different designs of deep networks using unidirectional or bidirectional layers of LSTM and GRU networks. These neural networks, specifically developed to learn long-term and short-term dependencies within sequential information such as time-series data, are successfully trained with the sensor signals of damaged FOWT. The sensor data were simulated due to the limited availability of field data from damaged FOWTs using multiple computational methods previously validated with experimental tests. The simulations accounted for the damage scenarios with various intensities, locations, and damage shapes, totaling 1320 damage scenarios. Both the presence of damage and its location were detected up to an accuracy of 94.8% using the best performing model of the selected network when tested for independent signals. The K-fold cross-validation accuracy of the selected network is estimated to be 91.7%. The presence of damage itself was detected with an accuracy of 99.9% based on the cross-validation regardless of the damage location. Structural damage detection using deep learning is not restricted by the assumptions of the systems or the environmental conditions as the networks learn the system directly from the data. The framework can be applied to various types of civil and offshore structures. Furthermore, the sequence-based modeling enables engineers to harness the vast amounts of digital information to improve the safety of structures.Published by Elsevier Ltd.