ABSTRACT During the last decades, the detection of damages in offshore platforms has been one of the most important issues in ocean engineering. To achieve the goal of the accurate identification of structural damages, the damage-sensitive features need to be extracted from the structural dynamic responses. However, it is well known that the changes in the structural dynamic responses do not depend only on the damage but also on the environmental conditions. Many researchers have pointed out that the principal component analysis (PCA) method is effective in discovering the hidden information from the grossed and blurred data. Additionally, the cross-correlation function has been proved effective in building structural characteristics by proceeding it between the dynamic responses from different structural positions. Thus, this paper presents a method in developing a novel PCA-based method for clarifying the structural health state from the structural characteristics built by the cross-correlation function. Such method was verified by using the numerical model of a jacket oil platform subjected to random wave excitations. Subsequently, the noise resistance of the proposed method was also investigated. The results show that the proposed method is available in detecting the structural damages under the influence of the random wave excitations.

中文翻译：

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随机波激励下导管架油平台结构损伤检测的互相关和基于PCA的异常值算法的组合方法

摘要 在过去的几十年中，海上平台损坏的检测一直是海洋工程中最重要的问题之一。为了实现结构损伤的准确识别，需要从结构动力响应中提取损伤敏感特征。然而，众所周知，结构动力响应的变化不仅取决于损伤，还取决于环境条件。许多研究人员指出，主成分分析（PCA）方法可以有效地从粗糙和模糊的数据中发现隐藏的信息。此外，通过在来自不同结构位置的动态响应之间进行处理，互相关函数已被证明在构建结构特征方面是有效的。因此，本文提出了一种开发基于 PCA 的新方法的方法，该方法用于从互相关函数构建的结构特征中阐明结构健康状态。通过使用受随机波激励的夹套油平台的数值模型验证了该方法。随后，还研究了所提出方法的抗噪性。结果表明，该方法可用于检测随机波激励作用下的结构损伤。还研究了所提出方法的抗噪性。结果表明，该方法可用于检测随机波激励作用下的结构损伤。还研究了所提出方法的抗噪性。结果表明，该方法可用于检测随机波激励作用下的结构损伤。