Research on sampling rate selection of sensors in offshore platform shm based on vibration

Abstract In vibration-based structural health monitoring (SHM), if the data are collected at a higher sampling rate from sensors, a large amount of computing resources and space resources will be consumed. For offshore platforms with limited space resources, there are many difficulties in long-term continuous monitoring. Aiming at this problem, the influence of sampling rate on structural damage identification is studied by comparing and analyzing the acceleration and posture sensors commonly used in offshore platform monitoring. In the simulation of parameter change of nonlinear system, random decrements and their spectrum characteristics are extracted from acceleration and displacement data respectively under different sampling rates, and the characteristics are classified by support vector machine (SVM) to identify the rule of change. The research of model experiment data also shows that the low sampling rate data can realize the identification of the damage, and the recognition degree of the low sampling rate data based on posture is not lower than that of the acceleration. The method is applied to the monitoring data analysis of FPSO soft york single-point mooring system and the damage situation is identical with the actual situation. The results show that the structural damage can be reflected from the low sampling rate posture monitoring data.