Blade Monitoring: A Critical Component for Wind Farm Success

Wind turbine blades face extreme conditions every day. Yet until recently, monitoring these components has relied heavily on annual visual inspections. A new collaboration between Turbit and Weidmüller Monitoring Systems demonstrates how continuous blade monitoring can influence wind farm operations and create new possibilities for risk management.

Traditional Blade Monitoring Falls Short

Daniel Schingnitz, Head of Sales and Marketing at Weidmüller Monitoring Systems, describes the industry shift: "Initially, customers were focusing on ice detection only due to regulations. But we've seen growing interest in condition monitoring systems over the past five to six years as blade repair costs increase significantly and lead times grow longer."

Traditional approaches present clear limitations:

• Manual inspections provide only snapshot views, missing damage progression

• Drone inspections offer better coverage, but still capture discrete moments in time

• Visual assessments can miss internal structural issues that develop gradually

"It's only a partial look at the status of the blade," Schingnitz explains. "You check the status for a small period in time. It's not continuous monitoring where you see damage growth over weeks or days."

Continuous Blade Monitoring with AI

The Turbit-Weidmüller collaboration addresses these gaps through continuous monitoring using piezo accelerometers installed inside turbine blades. These sensors, positioned approximately one-third along the blade length, capture vibrations in flap-wise and edge-wise directions at high frequency.

Dr. Richard Kunert, Turbit's Head of Data Science, explains their innovative approach: "We took inspiration from modern natural language processing models, the transformer architecture used in systems like ChatGPT. This technology excels at frequency and time series problems, making it surprisingly effective for analyzing blade vibration data."

The system works by learning the normal behavior patterns of healthy blades, then comparing real-time data against these baselines. When anomalies appear, operators receive immediate alerts rather than waiting for scheduled inspections.

Detecting Broader Turbine Issues

One unexpected discovery emerged during their pilot program with 30 turbines: blade sensors can detect problems beyond the blades.

"Vibration behavior of the blade is influenced by completely different external parameters," Schingnitz notes. "You can see deviations generated from the drivetrain in specific frequency ranges across all three blades. We're capable of analyzing deviations coming from drivetrain components. It doesn't replace dedicated drivetrain monitoring, but provides additional insight."

This broader detection capability means operators gain visibility into:

• Main bearing issues

• Drivetrain irregularities

• Blade bearing problems

• Structural anomalies

The pilot program identified at least two significant issues across the 30-turbine test fleet, validating the system's effectiveness even in relatively small portfolios.

From Reactive to Preventive

The business case for continuous blade monitoring extends beyond damage detection. Schingnitz estimates that operators typically face significant blade-related issues twice during a turbine's lifetime, making monitoring systems cost-effective through early intervention alone.

However, the economic benefits reach further:

Insurance Integration: Partnerships with insurers like HDI Global are creating new coverage models. Continuous

monitoring can reduce risk assessments, potentially improving insurance terms or enabling coverage for previously uninsurable scenarios.

Inspection Optimization: Traditional requirements for annual visual inspections—costly and dangerous for technicians—may be reduced or eliminated when continuous monitoring provides superior visibility.

Maintenance Planning: Early detection enables targeted repairs during planned maintenance windows rather than emergency shutdowns.

Looking Forward: Integrated Risk Management

The collaboration continues evolving toward more comprehensive monitoring. Weidmüller plans to introduce dual sensor positions per blade and three-dimensional MEMS sensors to improve damage localization precision.

From Turbit's perspective, blade monitoring represents another data source feeding their broader vision of integrated turbine intelligence. "We want artificial intelligence to combine information from different sources to create a holistic view of turbine health," Dr. Kunert explains.

This integration approach moves beyond siloed monitoring systems toward comprehensive risk infrastructure that operators can rely on for critical decisions.

Key Considerations for Wind Farm Operators

For operators evaluating blade monitoring solutions, several factors deserve attention:

Data Quality: The effectiveness of AI monitoring depends heavily on sensor reliability and data consistency.

Integration Capability: Solutions that combine multiple data sources (SCADA, vibration sensors, maintenance records) offer more complete insights than standalone systems.

Scalability: AI approaches that learn individual turbine behavior patterns scale more effectively than those requiring manual expertise for each installation.

Economic Justification: Beyond damage detection, consider insurance benefits, inspection cost savings, and maintenance optimization when calculating return on investment.

Building the Future of Wind Energy Operations

Combining proven sensor technology with advanced AI analysis creates new possibilities for wind farm operations. As turbines grow larger and operate in increasingly challenging conditions, continuous monitoring becomes essential infrastructure rather than an added benefit.

"Both companies came together to push the wind industry forward," Dr. Kunert reflects. "We've created a new product that can really make a difference in the market."

This collaboration represents a broader trend toward integrated risk management in wind energy, moving from reactive maintenance toward predictive operations that maximize asset value while minimizing unexpected failures. As the industry continues scaling toward 100% renewable energy, innovations like Blade Monitoring will become fundamental building blocks for reliable, efficient wind farm operations.

Want to learn more about how blade monitoring can benefit your wind farm operations? Listen to the full podcast episode where our CEO Michael Tegtmeier talks to Dr. Richard Kunert and Daniel Schingnitz, and dive deeper into the technical details and real-world applications of Blade Monitoring.