Detecting damage to offshore platform structures using the time-domain data

• Published in: Journal of marine science and application Vol. 7; no. 1; pp. 7 - 14
• Main Author: 程远胜 王真
• Format: Journal Article
• Language: English
• Published: Heidelberg Harbin Engineering University 01.03.2008; Faculty of Traffic Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
• Subjects: Electrical Machines and Networks; Engineering; Geotechnical Engineering & Applied Earth Sciences; Machinery and Machine Elements; Offshore Engineering; Power Electronics; 建筑物; 损伤检测; 检测技术; 近海平台; time series analysis; autoregressive coefficient; offshore platform; sensitivity analysis; TU311; damage detection; time-domain response; O211.6
• ISSN: 1671-9433; 1993-5048
• DOI: 10.1007/s11804-008-7046-z

A new method that uses time-domain response data under random loading is proposed for detecting damage to the structural elements of offshore platforms. In our study, a time series model with a fitting order was first constructed using the time-domain of noise data. A sensitivity matrix consisting of the first differential of the autoregressive coefficients of the time series models with respect to the stiffness of structural elements was then obtained based on time-domain response data. Locations and severity of damage may then be estimated by solving the damage vector whose components express the degrees of damage to the structural elements. A unique aspect of this detection method is that it requires acceleration history data from only one or a few sensors. This makes it feasible for a limited array of sensors to obtain sufficient data. The efficiency and reliability of the proposed method was demonstrated by applying it to a simplified offshore platform with damage to one element. Numerical simulations show that the use of a few sensors＇ acceleration history data, when compared with recorded levels of noise, is capable of detecting damage efficiently. An increase in the number of sensors helps improve the diagnosis success rate.