Damage Detection from Operational Wind Turbine Blades

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Research Team: Dr. Murat Inalpolat and Dr. Christopher Niezrecki, University of Massachusetts Lowell

The team at University of Massachusetts Lowell is currently developing and testing the feasibility of an integral acoustic sensing based wind turbine blade structural health monitoring system that can be installed on both new and existing wind turbines (retrofit). Current utility blade testing, in-situ health monitoring and damage detection is a bottleneck to the certification and advancement of new blade designs and materials as well as reliable operation of wind turbines. The proposed new technology is expected to address the need for a robust, reliable and low cost blade condition monitoring and operational damage detection system. This innovative technology was initially proposed by the PIs (Drs. Murat Inalpolat and Christopher Niezrecki) and recently disclosed through an international patent application (PCT/US14/62329).  Because of continually varying operating conditions, all blades will experience leading and trailing edge splits, cracks, or holes that are currently not detectable except by visual inspection or post blade failure. The proposed innovative system will address this need and utilizes low-cost, low-maintenance microphones for passive monitoring of natural flow-induced noise (due to wind) that couples with the structural damage, and acoustic sources to excite the blade’s cavity structure from within for active monitoring. The blade damage will manifest itself in changes to the acoustic cavity frequency response functions and to the blade acoustic transmission loss (sound level drop across the composite structure). This technology depends on a fundamentally simple but effective idea, utilizes low cost wireless sensors and sound sources with limited to no cabling, and with easy maintenance features (equipment close to blade roots etc.).

Wind Blade Graph                   Wind Blade Photo

 

 

 

 

 

 The financial support from NSF CMMI  through Award #1538100 made this work possible and is gratefully acknowledged by the team.

NSF