ABSTRACT
As the global leader in both installed wind power capacity and turbine quantity, China is witnessing megawatt-class wind turbines emerge as pivotal enablers in building its new energy ecosystem under the “Dual Carbon” strategic framework. AI and robtostic technology, serving as a transformative digital solution for full lifecycle management of large-scale energy equipment, drives cross-domain innovations spanning dynamic simulation, model-driven design, intelligent manufacturing, optimized control, and predictive operation and maintenance (O&M). This presentation explores cutting-edge advancements in autonomous maintenance for offshore wind turbines, integrating artificial intelligence (AI) and robotic systems to overcome the high costs, safety risks, and logistical  challenges of traditional methods. Key innovations include remote monitoring, AI-enabled autonomous vehicles and remotely operated vehicles (ROVs) capable of performing precise inspections, cleaning, and repairs in turbulent ocean environments. Concurrently, AI-driven predictive maintenance frameworks—using data-physics hybrid models like multi-task Gassian process and physics-informed neural networks—integrate physics, SCADA and sensor data to detectcomponent anomalies (e.g., bearing failures) up to 7 days in advance, optimizing intervention timing. These systems leverage machine learning algorithms and 3D semantic mapping to stabilize operations amid currents, navigate complex structures, and execute tasks such as blade defect remediation with minimal human intervention. Together, these intelligence and robotic solutions enhance turbine reliability, slash operational costs, and mitigate safety hazards, positioning autonomous systems as pivotal for sustainable offshore wind expansion.