Vibration-based structural health monitoring of offshore pipelines: Numerical and experimental study

The world economy is heavily dependent upon an extensive network of pipelines for energy sources transmission. Because the consequence of pipeline failure could be disastrous both economically and environmentally, it is imperative to maintain pipeline integrity and reliability. An integrated autoregressive moving average (ARMA) model algorithm is developed in this study for the structural health monitoring (SHM) of offshore pipelines. In this method, a signal pre-processing module is integrated to remove the influence of loading conditions and noise on structural response. Auto-correlation function of the pre-processed signal is utilized as a substitute of analysis input to overcome noise effect and bias in ARMA model fitting. PAF method is employed for establishing optimal ARMA model, which autoregressive model parameters are extracted as feature vector. A damage indicator based on Mahalanobis distance is defined for damage detection and localization. To demonstrate the reliability of the method in detecting conditions of subsea pipelines, both numerical simulations of pipeline vibrations under ambient marine environment and laboratory tests of a scaled pipeline model in a large wave tank are carried out. The numerically simulated and the measured vibration data in wave tank tests are analyzed using the proposed method in this study to detect pipeline conditions. The results demonstrate that the proposed approach is robust and very sensitive to subsea pipeline damage. It provides accurate identification of damage existence and damage locations in offshore pipeline system. The proposed approach can be used for online SHM of offshore pipeline structures, as well as other civil structures