Improved condition monitoring for an FPSO system with multiple correlated components

Abstract

It is expected that a resilient condition monitoring (CM) system can identify the faults of sensors and key components, assess the system risk level based upon multichannel monitoring data, and recover missing real data when complete sensor failure occurs. As for CM of an offshore system, sensors are usually less reliable than the components being monitored. Without CM resilience, the whole system will be vulnerable to accidents and different types of disturbance. This paper provides a statistical and artificial intelligence approach to realizing resilient CM for offshore systems with multiple correlated components. This approach enables identifying sensor faults, damage and degradation of key components through frequency-centered wavelet analysis along with an artificial neural network-based dynamic threshold under different operating environments. More importantly, the proposed method enables the recovery of missing real data based on fusion data from other normal sensors and multivariate Autoregressive Moving Average (ARMA) model. The method is applied to a hawsers’ CM system in a Floating Production, Storage and Offloading (FPSO) oil offloading system. The numerical results illustrate the feasibility and effectiveness of the proposed method in realizing CM resilience for such complex and critical systems.