A significant number of wind turbines deployed across the Penghu Islands are exposed to challenging coastal and marine conditions. To address the operational risks associated with such environments, in this study, we introduce a real-time monitoring and fault detection framework aimed at identifying abnormal turbine behavior, particularly those linked to blade damage. The proposed system enhances turbine efficiency and reliability by continuously assessing blade integrity, swiftly detecting irregularities, and supporting near-instantaneous maintenance actions. This capability is especially vital in disaster-prone regions, where turbines may encounter conditions exceeding standard design specifications. The framework integrates sensor-based data acquisition—capturing input from accelerometers, anemometers, hygrometers, thermometers, and barometers—with signal processing and neural network techniques to analyze three-axis vibration data. By identifying distinct patterns associated with blade faults, the system enables timely detection and reporting of malfunctioning units, thereby facilitating effective repair and operational continuity.