Intelligent Supervision and Integrated Fault Detection and Diagnosis for Subsea Control Systems

This paper proposes an artificial intelligence based framework for integrated fault detection and diagnosis to support supervision and decision making applicable to subsea control systems. Instrumentation, electrical, electronic, hydraulic and communication subsystems are considered. This approach may contribute to minimizing well shut-down and production losses due to unexpected faults on any subsea control system component or subsystem. It may also contribute to achieving incipient fault detection and appropriate fault identification to support and improve troubleshooting, decision making and maintenance tasks (preventive maintenance).