为了确保海洋平台安全作业，及时辨识损伤以及进行损伤定位，海洋平台结构健康监测技术已成为学者研究关注的重要问题。针对某在役导管架平台，对平台在不同随机波浪激励下的动力响应分别进行了健康状态和损伤状态的数值模拟。在损伤辨识过程中，对结构不同位置的动力响应进行互相关分析，提取损伤敏感特征；利用主成分分析（principal component analysis ，PCA）方法从复杂的数据中提取主成分；定义损伤指标并进行损伤辨识。针对传统PCA方法对某些杆件的损伤辨识精度不高等问题，提出了一种新的主成分选取方式，并在此基础上对传统PCA方法进行了改进。结果表明，改进后的PCA方法有效提高了损伤辨识的精度，可以对随机波浪条件下的结构损伤进行准确辨识。
Abstract
In order to ensure safety of offshore platform, identify and locate its damage in time, the structural health monitoring technology of offshore platform has become an important issue for scholars. Here, for a jacket platform in service, its dynamic responses under different random wave excitations were numerically simulated in health state and damage state, respectively. In the process of damage identification, cross-correlation analyses of dynamic responses of different positions of the platform were performed to extract damage sensitive features. Then the principal component analysis (PCA) method was used to extract principal component from complex data. Finally, the damage index was defined and the damage identification was done. Aiming at the problem of the traditional PCA method’s accuracy for damage identification of some members being not high, a new method of selecting principal components was proposed to improve the traditional PCA method. The results showed that the improved PCA method can effectively improve the accuracy of damage identification to correctly identify structural damage under random wave condition.
关键词
互相关函数 /
主成分分析（PCA） /
平台结构 /
损伤辨识
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Key words
cross-correlation function /
principal component analysis (PCA) /
platform structure /
damage identification
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