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Deep learning methods for damage detection of jacket-type offshore platforms
Process Safety and Environmental Protection ( IF 7.8 ) Pub Date : 2021-08-27 , DOI: 10.1016/j.psep.2021.08.031
Xingxian Bao 1, 2 , Tongxuan Fan 1 , Chen Shi 3 , Guanlan Yang 1
Affiliation  

Recently, big data and machine learning based damage detection methods to support risk management of offshore facilities have received great attention, compared to traditional modal parameters-based methods. This paper illustrates the application of deep learning methods in damage detection of offshore platforms using measured vibration response of the structures subjected to random excitations. The numerical example of a jacket-type offshore platform under random wave excitation is applied to verify the applicability of convolutional neural network (CNN), long short-term memory (LSTM) networks, and CNN-LSTM method. The comparison of the three approaches are conducted in terms of accuracy and efficiency of damage localization and severity estimation for the simulated damage cases. In addition, the random decrement technique (RDT) for data preprocessing is used to improve the capability of damage detection of the three deep learning methods in noisy conditions. Moreover, the proposed RDT combined with the deep learning methods are applied to laboratory tests of a jacket platform model under random loading produced by a shaking table. Minor and major damages at different locations are discussed. Results show that the proposed combination method has an outstanding performance in structural damage detection even in noisy conditions, and also has great potential application in industrial process safety and operational risk management.



中文翻译:

导管架式海上平台损伤检测的深度学习方法

最近,与传统的基于模态参数的方法相比,基于大数据和机器学习的损伤检测方法来支持海上设施的风险管理受到了极大的关注。本文说明了深度学习方法在海上平台损伤检测中的应用,利用随机激励下结构的实测振动响应。应用随机波激励下导管架式海上平台的数值例子验证了卷积神经网络(CNN)、长短期记忆(LSTM)网络和CNN-LSTM方法的适用性。在模拟损伤情况下,在损伤定位和严重程度估计的准确性和效率方面对三种方法进行了比较。此外,用于数据预处理的随机递减技术(RDT)用于提高三种深度学习方法在噪声条件下的损伤检测能力。此外,所提出的 RDT 结合深度学习方法被应用于在振动台产生的随机载荷下导管架平台模型的实验室测试。讨论了不同位置的轻微和重大损坏。结果表明,所提出的组合方法即使在嘈杂的条件下也能在结构损伤检测方面具有突出的性能,在工业过程安全和操作风险管理方面也具有巨大的应用潜力。所提出的 RDT 结合深度学习方法应用于在振动台产生的随机载荷下导管架平台模型的实验室测试。讨论了不同位置的轻微和重大损坏。结果表明,所提出的组合方法即使在嘈杂的条件下也能在结构损伤检测方面具有突出的性能,在工业过程安全和操作风险管理方面也具有巨大的应用潜力。所提出的 RDT 结合深度学习方法应用于在振动台产生的随机载荷下导管架平台模型的实验室测试。讨论了不同位置的轻微和重大损坏。结果表明,所提出的组合方法即使在嘈杂的条件下也能在结构损伤检测方面具有出色的性能,在工业过程安全和操作风险管理方面也具有巨大的应用潜力。

更新日期:2021-09-02
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