Remaining Useful Life Estimation of Structure Systems Under the Influence of Multiple Causes: Subsea Pipelines as a Case Study

In dynamic complex environments, the degradation of structure systems is generally caused not by a single factor but by multiple ones, and the process is subject to a high level of uncertainty. This article contributes a hybrid physics-model-based and data-driven remaining useful life (RUL) estimation methodology of structure systems considering the influence of multiple causes by using dynamic Bayesian networks (DBNs). The structure model and parameter model of DBNs for the degradation process caused by a single factor are established on the basis of theoretical or empirical physical models, thereby solving the problem of insufficient data. An RUL estimation model is subsequently established by integrating these degradation process models. The RUL value is obtained from the time difference between the detection point and predicted failure point, which is determined using the failure threshold of performance. The sensor data and expert knowledge can be input into the estimation model to update the RUL value whenever necessary. The subsea pipelines in offshore oil and gas subsea production systems are adopted to demonstrate the proposed methodology. The degradation processes with fatigue, corrosion, sand erosion, and internal waves are modeled using DBNs, and the RUL is estimated using a DBN-based RUL methodology.