Cross-validation enhanced digital twin driven fault diagnosis methodology for minor faults of subsea production control system

A subsea production system is essential for the subsea production of oil and gas. Real-time monitoring can ensure safe production. A subsea production control system is the core of the subsea production system and the top priority to be monitored. Minor faults refer to faults with weak characteristics and are difficult to be found. They are common and pose significant risks in subsea production system. Safe operation of the system can be guaranteed to the greatest extent with a timely detection and handling of minor faults. Digital Twin driven fault diagnosis is an effective method to monitor the subsea production control system. However, the combination of digital twin and fault diagnosis is not comprehensive, especially in data interaction. It leads to the fact that digital twin cannot improve fault diagnosis accuracy significantly, mainly when a minor fault occurs. A cross-validation enhanced digital twin-driven fault diagnosis methodology for minor faults of the subsea production control system is proposed to achieve high accuracy. Control, loss and fault parameters are presented and used for building a digital twin model. Bayesian Networks are used to construct a fault diagnosis model. Single and cumulative errors are used to measure the difference between digital twin models and physical systems. A verification feedback method is used to check the diagnosis results. Data of four days of a subsea production system in the South Sea of China is used to demonstrate the proposed methodology. The results show that the method can improve the diagnosis accuracy for minor faults effectively.