This chapter introduces two fault diagnosis models for two types of subsea control systems. To address the limitations posed by a finite number of sensors and the concealment of faults, a three-model-driven fault diagnosis model with regard to energy, fluid and information is proposed for complex hydraulic control system. The diagnostic model is finalized through the establishment of a fault reasoning model using a Bayesian network. To solve the problem of unclear fault characteristics in multi module redundant systems. A model-driven methodology is proposed to the fault diagnosis of complete closed-loop control systems configured by multiple modular redundant technology. Multi monitoring information together with the basic information is used for fault symptom input. Inference probability is converted into diagnostic results. A dual redundant control system for subsea blowout preventers is used to study the performance of the proposed methods. The results indicate that the methods are effective for fault diagnosis of complex hydraulic control system and redundant closed-loop control systems.