Operational modal characterisation for long-term monitoring in offshore structures

Abstract

In the effort to increase sustainability and help drive the energy sector to support oil and gas decarbonisation, the importance of identifying and characterising the current state of assets is becoming ever more crucial for operators, especially with regards to life extension and repurposing. Structural Health Monitoring (SHM) offers a promising and forward-thinking methodology for tracking asset integrity. In particular, Operational Modal Analysis (OMA) presents a proven, output only method for structure characterisation and modal tracking. However, Environmental and Operational Variations (EOV) can influence the modal properties of the structure making modal parameter estimation and tracking less reliable in the short- and long-term. This study employs an Automated Operational Modal Analysis (AOMA) methodology based on the combination of stochastic subspace identification and natural frequency histogram bin analysis for robust parameter extraction. The methodology is implemented in a complex of three bridge linked offshore platforms. Structural complexity, operational loading and significant modal couplings require sophisticated analysis. The preliminary results show discontinuity of the operational modes over a 6-month long monitoring period. The findings demonstrate the need of further analysis to understand the time-variant parameters which determine structural response across the asset lifetime.