Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in the academic community. We look forward to seeing you there!
PO016: Generating tools to effectively compare and rank blade acoustic response: Defining Probability of Defect Activity (PDA).
Valery Godinez-Azcuaga, VP Engineering and Product Development, Mistras Group
Abstract
Health or comparative indices are commonly developed in Statistics and Data Analytics to allow comparison and optimize selection of assets, sport players, or investment opportunities. The data used to develop such an index depends on relevant factors correlated to specific performance or behaviors. This work introduces and describes an acoustic health quantity developed to track the onset and progression of defect/damage on wind turbine blades. Such index: PDA or Probability of Defect Activity, is calculated for each blade using the acoustic activity collected by the SENSORIATM blade monitor, a blade health condition monitoring system based on the Acoustic Emission (AE) technology. AE on wind turbine blades considers that defects/damages in structures act as stress intensifiers; even when the overall load on the structure is below dangerous levels, local stresses at the edges of the defects may raise to dangerous values. Under these conditions, defects become "active" and produce AE. In addition, the presence of some blade damages can affect the aerodynamic response of the blade, and in turn change the blade background noise. Tracking transient and dynamic change in acoustic signatures of a wind turbine blade overtime offers a possibility to detect damage onset and track follow progression/evolution. Continuous monitoring of acoustic signatures and background noise can be used by asset owners to prioritize inspection and manage asset operation. Blade PDA can be used to identify blades that present actively growing damage, which are responsible for producing increased acoustic activity in response to changes in blade loading. The changes in loading are produced by changes in the weather, such as wind speed or direction, or by changes in turbine operation. The blade PDA involves several acoustic signal features and considers the presence of 3 types of acoustic signal populations separated by frequency response.
