A Dynamic-Bayesian-Networks-Based Resilience Assessment Approach of Structure Systems: Subsea Oil and Gas Pipelines as A Case Study

Under unanticipated natural disasters, any failure of structure components may cause the crash of an entire structure system. Resilience is an important metric for the structure system. Although many resilience metrics and assessment approaches are proposed for engineering system, they are not suitable for complex structure systems, since the failure mechanisms of them are different under the influences of natural disasters. This paper proposes a novel resilience assessment metric for structure system from a macroscopic perspective, named structure resilience, and develops a corresponding assessment approach based on remaining useful life of key components. Dynamic Bayesian networks(DBNs) and Markov are applied to establish the resilience assessment model. In the degradation process, natural degradation and accelerated degradation are modelled by using Bayesian networks, and then coupled by using DBNs. In the recovery process, the model is established by combining Markov and DBNs. Subsea oil and gas pipelines are adopted to demonstrate the application of the proposed structure metric and assessment approach.

数据驱动的液压系统早期微小泄漏检测与定位方法

泄漏是引起液压系统失效最主要的原因,泄漏的精确诊断与定位对保障液压系统的正常运行具有重要意义。在泄漏发生早期,液压系统的压力信号没有明显变化,压力传感器难以对其进行监测。同时,系统的压力受到负载及开关状态改变而变化,这将导致传感器信号变化剧烈而进一步加剧泄漏检测与定位难度。本文提出了一种数据驱动的液压系统早期微小泄漏检测与定位方法,利用小波变化对信号进行降噪处理,建立基于Bayesian网络的泄漏检测与定位模型进行泄漏检测,通过归一化模型将不同压力下声发射信号特征值转化为目标压力下声发射信号特征值,以提高压力鲁棒性。该方法在实验室的液压系统上得到了验证。