A wind turbine is an example of a Cyber-Physical System, where sensors embedded in components like the structure, rotors, drivetrain, generators, and controllers collect data on structural (e.g., nacelle and generator temperature), environmental (e.g., ambient temperature, humidity, pressure), and operational (e.g., rotor speed, pitch angle, nacelle direction) conditions. This data is analysed using algorithms to recommend optimal component settings and support decision-making. These recommendations enable automatic or remote control of key functions, such as pitch adjustment, rotor speed regulation, and grid connectivity. By integrating interconnected technologies, the system enhances overall performance and optimizes power generation. The core components of a Cyber-Physical System include Condition Monitoring, SCADA, and Digital Twin technologies. The Digital Twin serves as a virtual counterpart, replicating the physical asset by receiving real-time sensor data from Condition Monitoring and SCADA systems. It dynamically simulates behaviour, assesses current conditions, and predicts future states under various scenarios. This predictive capability enables proactive adjustments to improve efficiency, resilience, and performance. Additionally, the system leverages data to develop optimized technical asset management strategies. This paper presents a novel framework, inspired by human cognitive processes, for an Integrated Condition Management System that combines Condition Monitoring, SCADA, and Digital Twin technologies. By mimicking human cognitive functions—perception, learning, reasoning, and decision-making—the system enhances operations, detects anomalies, predicts failures, and recommends optimized maintenance strategies. This approach aims to improve efficiency, reliability, and proactive asset management. While wind turbines serve as a primary use case, the framework’s potential applications extend across various industrial systems.