Offshore platforms are susceptible to structural damage due to prolonged exposure to random loads,such as wind,waves,and currents.This is particularly true for platforms that have been in service for an extended perio...Offshore platforms are susceptible to structural damage due to prolonged exposure to random loads,such as wind,waves,and currents.This is particularly true for platforms that have been in service for an extended period.Identifying the modal parameters of offshore platforms is crucial for damage diagno sis,as it serves as a prerequisite and foundation for the process.Therefore,it holds great significance to prioritize the identification of these parameters.Aiming at the shortcomings of the traditional Fast Bayesian Fast Fourier Transform(FBFFT) method,this paper proposes a modal parameter identification method based on Automatic Frequency Domain Decomposition(AFDD) and optimized FBFFT.By introducing the AFDD method and Powell optimization algorithm,this method can automatically identify the initial value of natural frequency and solve the objective function efficiently and simply.In order to verify the feasibility and effectiveness of the proposed method,it is used to identify the modal parameters of the IASC-ASCE benchmark model and the j acket platform structure model,and the Most Probable Value(MPV) of the modal parameters and their respective posterior uncertainties are successfully identified.The identification results of the IASC-ASCE benc hmark model are compared with the identification re sults of the MODE-ID method,which verifies the effectivene ss and accuracy of the proposed method for identifying modal parameters.It provides a simple and feasible method for quantifying the influence of uncertain factors such as environmental parameters on the identification results,and also provide s a reference for modal parameter identification of other large structures.展开更多
为及时对天线罩结构老化及损伤情况预警,需要实时、稳定地对天线罩结构状态进行监测。本天线罩结构损伤分析系统基于自动频域分解法(Automatic Frequency Domain Decomposition-AFDD)和卡尔曼滤波算法,使用加速度传感器和采集仪,通过对...为及时对天线罩结构老化及损伤情况预警,需要实时、稳定地对天线罩结构状态进行监测。本天线罩结构损伤分析系统基于自动频域分解法(Automatic Frequency Domain Decomposition-AFDD)和卡尔曼滤波算法,使用加速度传感器和采集仪,通过对多组实时采集的加速度传感器数据进行处理,得到反应天线罩结构老化和损伤的各种相关数据,并实时将天线罩结构状态在人机界面显示、对异常状态预警。在此基础上,分析了由加速度传感器输出数据经处理得到的不同参数对于天线罩损伤情况的敏感程度,建立多参数的联合预警机制,实现系统面对不同损伤时的实时判断和预警。展开更多
基金financially supported by the Natural Science Foundation of Heilongjiang Province of China (Grant No. LH2020E016)the National Natural Science Foundation of China (Grant No.11472076)。
文摘Offshore platforms are susceptible to structural damage due to prolonged exposure to random loads,such as wind,waves,and currents.This is particularly true for platforms that have been in service for an extended period.Identifying the modal parameters of offshore platforms is crucial for damage diagno sis,as it serves as a prerequisite and foundation for the process.Therefore,it holds great significance to prioritize the identification of these parameters.Aiming at the shortcomings of the traditional Fast Bayesian Fast Fourier Transform(FBFFT) method,this paper proposes a modal parameter identification method based on Automatic Frequency Domain Decomposition(AFDD) and optimized FBFFT.By introducing the AFDD method and Powell optimization algorithm,this method can automatically identify the initial value of natural frequency and solve the objective function efficiently and simply.In order to verify the feasibility and effectiveness of the proposed method,it is used to identify the modal parameters of the IASC-ASCE benchmark model and the j acket platform structure model,and the Most Probable Value(MPV) of the modal parameters and their respective posterior uncertainties are successfully identified.The identification results of the IASC-ASCE benc hmark model are compared with the identification re sults of the MODE-ID method,which verifies the effectivene ss and accuracy of the proposed method for identifying modal parameters.It provides a simple and feasible method for quantifying the influence of uncertain factors such as environmental parameters on the identification results,and also provide s a reference for modal parameter identification of other large structures.
文摘为及时对天线罩结构老化及损伤情况预警,需要实时、稳定地对天线罩结构状态进行监测。本天线罩结构损伤分析系统基于自动频域分解法(Automatic Frequency Domain Decomposition-AFDD)和卡尔曼滤波算法,使用加速度传感器和采集仪,通过对多组实时采集的加速度传感器数据进行处理,得到反应天线罩结构老化和损伤的各种相关数据,并实时将天线罩结构状态在人机界面显示、对异常状态预警。在此基础上,分析了由加速度传感器输出数据经处理得到的不同参数对于天线罩损伤情况的敏感程度,建立多参数的联合预警机制,实现系统面对不同损伤时的实时判断和预警。
