Acoustic emission sensing for wind turbine blade monitoring

Sensor on a blade

What is in common between a pencil lead and Optics11 acoustic emission sensor? We recently used a pencil lead on a wind blade to test our sensor’s sensitivity and crack detection capability. Read the full story below.

Wind turbines maintenance expenses can annually add up to thousands of euros per blade. Therefore, a recent Globalstars Blade Monitoring project driven by the manufacturing challenges, gathered several international partners to focus on the structural health of the wind turbine blade.

The project brought together a consortium of international partners, including two Taiwanese actors: DSP, NARLabs, and three Dutch companies: Optics11, Somni Solutions and PhotonFirst, and the Dutch research Institute TNO, each contributing their unique expertise to the project. The main office at NCREE Taipei and their test facility in Tainan, Taiwan have played pivotal roles in the development and testing of this innovative technology. The collaboration was marked by a shared commitment to advancing wind blade monitoring technology. 

Objectives and Preparations

The test campaign, conducted in collaboration with TNO, aimed to advance the Structural Health Monitoring (SHM) of offshore wind turbine blades. The primary objective of the test campaign was to leverage collected data to create a predictive twin using the Bayesian inference procedure. The team developed a parametric turbine blade FEM and tuned the updating procedure using measurement data. The preparations also included generating synthetic data to validate the model updating procedure, which was compared to a “split train test” method commonly used in machine learning. 

Acoustic Emission Sensors

Optics11 deployed our OptimAE system with 6 fiber optic acoustic emission sensors. The acoustic emission sensors are measuring acoustic waves created by crack generation and propagation within the material of the blade. Since the wind blade is constructed of a composite (anisotropic) material, there is a need for optimal sensor placement and sensor coverage identification. Five acoustic emission sensors were placed in proximity of the loading brackets, and a single sensor at the root of the blade, where numerical analysis identified local stress maxima.

Optics11 acoustic emission sensors have once again proven their top-notch performance. Considering the size of wind turbine blades, precision in detecting early cracks is crucial. That is where the pencil lead came in handy! In a simple test, by cracking the lead of a mechanical pencil away from the Optics11 sensor, our team measured the maximum distance we can detect the break from different directions. And it added up to impressive several meters along the blade. When something as tiny as a crack of a pencil lead can be picked up by an acoustic emission sensor, we can ensure an optimal position of the sensors on the blade to perform blade monitoring.

Acoustic emission sensor positioned on a blade after a pencil test

In addition to Optics11 acoustic emission sensors, 10 FBG strain sensors, 2 FBG temperature sensors, 4 FBG based accelerometers: 2x2D sensors, 2x1D sensors from Somni Solutions and 5 electrical based draw wire sensors from TNO were installed. These sensors were strategically placed on the wind turbine blade to create its digital twin with highest possible accuracy. The installation process was designed to ensure that all the sensors provide accurate and reliable data for such predictive maintenance model. 

Wind Blade Tests

Operational loading of the blade was simulated by a discretized static load application, calculated to induce the necessary stress levels. Dynamic loading was introduced as a free vibration. After aerodynamic load calculation, results were used to augment the machine learning model developed for Structural Health Monitoring (SHM) activities. 

The project leveraged advanced data analytics and artificial intelligence to analyze the sensor data. This analysis was essential for the SHM and predictive maintenance of the wind turbine blades, allowing for more efficient maintenance strategies and extending the life cycles of these critical components.

Conclusion

The wind blade monitoring project stands as a testament to the power of collaboration and innovation in the renewable energy sector. The role of Optics11 in this project has not only contributed to technological advancements but also underscored the company’s commitment to sustainability and excellence in the industry.

In conclusion, the wind blade monitoring project achieved significant milestones and provided valuable lessons for future endeavors.

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