Collaborative Acoustic–Visual Monitoring for Offshore Wind Turbine Blade Condition Based on IEEE 1451 Standard

Monitoring offshore wind turbine blades’ health conditions is a crucial task. With the increasing demand for such systems, there are rising requirements for their reliability and efficiency, especially for wind turbine operation. This article proposes a wind turbine blade health monitoring system design based on IEEE P1451.9, an active standard project in the IEEE 1451 smart sensor standard family, using smart acoustic and visual sensors for collaborative monitoring. Our research mainly provides a sensor strategy that detects passive noise emitted by wind turbine blades through machine acoustics and triggers a camera for video recording and blade tip trajectory calculation when a certain threshold is reached. This is the first time an application demonstration of this kind has been featured in IEEE P1451.9. By leveraging the data-processing capabilities of the smart sensors, the proposed system applies edge computing for processing both acoustical and visual signals. Acoustic data- downsampling-windowed dBA and zero-crossing rate (ZCR) are used to extract features before transmitting them to the monitoring terminal while reducing overall data volume. Anomalies in the feature data are used to trigger the activation of visual sensors. The blade tip trajectory is then converted into 1-D feature curves using digital image-processing algorithms, thereby reducing communication resource consumption. Elements from the latest draft of the IEEE P1451.9 standard are integrated into the system, enabling the efficient transmission of sequence data and facilitating unified communication between acoustic and visual signal-processing components. Consequently, the system’s functionality is validated using actual wind turbine blade sound and video data. The testing process covers all aspects, including establishing connections, edge computing, data transmission, collaborative activation, and display, demonstrating the IEEE P1451.9 smart sensors’ feasibility and practicality.