Uncertainty-Aware Safety Assessment of Offshore Jacket Platforms via Signal Trend Decomposition and Bi-Objective Interval Optimization

Abstract Structural health monitoring of offshore jacket platform is crucial to ensure the safety of offshore oil and gas development. At this stage, the judgment of platform structural safety state based on monitoring data mainly focuses on deterministic prediction, which often neglects the uncertainty and trend of safety state changes. So a state detection model for offshore jacket platform based on signal trend feature extraction is proposed in this paper. Firstly, the Variational Modal Decomposition (VMD) along with Harris Hawk Optimization (HHO) was combined in this model, which was used to decompose the initial data into Intrinsic Mode Function (IMF) with clearer trends. Subsequently, the Holt-Winters algorithm is utilized to extract trend information from the historical data to predict the possible future changes of the IMF. Further, the Holt-Winters projection results under two different trend parameters are input into the Regularized Extreme Learning Machine (RELM) to obtain the prediction intervals for the corresponding moments. Finally, a bi-objective optimization model with identification accuracy and interval width as the dual objectives is constructed to determine the reasonable interval of the platform's state change during operation, in order to monitor its safety state. The analysis results show that the proposed method achieves excellent recognition accuracy while the interval width is greatly reduced, which significantly improves the credibility of the model and can provide theoretical support for the state detection of offshore jacket platforms.