A Volterra Kernel Energy-Based Health Monitoring Approach for Carbon Fiber-Reinforced Polymer Mooring Systems of Deepwater Floating Platforms

Mooring systems are critical to deepwater floating platforms, ensuring their safe operation under harsh marine environmental conditions. Mooring-line failure can disrupt load redistribution, alter platform dynamic response, and compromise operational safety. Conventional monitoring methods based on maximum mooring tension and tension responses often fail to distinguish environmental load variations from structural abnormalities. This study proposed a health-monitoring approach using Volterra kernel energies extracted from mooring tension responses. A second-order truncated Volterra model with a self-excitation modeling strategy was employed to derive first- and second-order Volterra kernel energies, which were used to construct health monitoring indicators. The performance of these indicators was assessed using a semi-submersible floating platform equipped with a carbon fiber-reinforced polymer (CFRP) mooring system. Furthermore, the effects of sensor location, segment duration, and random seed on monitoring performance were investigated. The results indicated that the first-order Volterra kernel energy was highly sensitive to environmental load variations, whereas the second-order kernel energy effectively captured nonlinear dynamic characteristics associated with mooring-line failure. By integrating these complementary features, the proposed approach can effectively distinguish environmental load variations from structural abnormalities under complex marine environmental conditions. These findings provide a methodological basis for the health-state assessment and integrity management of deepwater mooring systems.