New Technology Predicts Wind Turbine Lifespan for Cost Savings
The Hague, Thursday, 20 March 2025.
Sensing360, backed by innovation subsidies, has developed technology to predict the remaining lifespan of wind turbines accurately, allowing operators to reduce costs by avoiding unnecessary replacements.
Critical Innovation for European Wind Energy
The timing of this technological breakthrough is crucial, as Europe faces the challenge of replacing approximately 70,000 wind turbines in the coming years [1]. Through an innovative monitoring system developed since 2018, Sensing360’s technology utilizes fiber optic sensors wrapped around a turbine’s gearbox to measure critical parameters including forces, rotational speed, temperature, and gearbox deformation [1].
Comprehensive Monitoring Solution
The system’s capabilities extend beyond basic monitoring. In collaboration with partner Somni Solutions, Sensing360 is developing a complete turbine monitoring solution that incorporates software providing operators with comprehensive insights into turbine performance [1]. The technology’s effectiveness is currently being validated through tests at existing onshore wind turbines in Great Britain and at Wageningen University [1].
Industry Recognition and Future Applications
The innovation’s potential has already gained industry recognition, with Sensing360 receiving a nomination for the Offshore Wind Innovators Awards [1]. This acknowledgment comes at a time when the wind energy sector is increasingly focused on component fatigue and life extension, as evidenced by dedicated sessions at major industry conferences in 2025 [2]. The technology aligns with broader industry trends toward predictive maintenance and condition monitoring in drivetrain technology [2].
Research-Backed Reliability
The development comes amid significant advances in wind turbine reliability assessment, supported by recent academic research. A 2025 study published in Renewable and Sustainable Energy Reviews demonstrates the growing importance of sophisticated monitoring systems for offshore wind turbines, particularly in preventing failures and optimizing maintenance strategies [4]. This approach to predictive maintenance aligns with sustainable development goals by extending asset lifetimes and reducing material waste [5].