Neuro-fuzzy computing for vibration-based damage localization and severity estimation in an experimental wind turbine blade with superimposed operational effects

Simon Hoell, Piotr Omenzetter

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Abstract

Fueled by increasing demand for carbon neutral energy, erections of ever larger wind turbines (WTs), with WT blades (WTBs) with higher flexibilities and lower buckling capacities lead to increasing operation and maintenance costs. This can be counteracted with efficient structural health monitoring (SHM), which allows scheduling maintenance actions according to the structural state and preventing dramatic failures. The present study proposes a novel multi-step approach for vibration-based structural damage localization and severity estimation for application in operating WTs. First, partial autocorrelation coefficients (PACCs) are estimated from vibrational responses. Second, principal component analysis is applied to PACCs from the healthy structure in order to calculate scores. Then, the scores are ranked with respect to their ability to differentiate different damage scenarios. This ranking information is used for constructing hierarchical adaptive neuro-fuzzy inference systems (HANFISs), where cross-validation is used to identify optimal numbers of hierarchy levels. Different HANFISs are created for the purposes of structural damage localization and severity estimation. For demonstrating the applicability of the approach, experimental data are superimposed with signals from numerical simulations to account for characteristics of operational noise. For the physical experiments, a small scale WTB is excited with a domestic fan and damage scenarios are introduced non-destructively by attaching small masses. Numerical simulations are also performed for a representative fully functional small WT operating in turbulent wind. The obtained results are promising for future applications of vibration-based SHM to facilitate improved safety and reliability of WTs at lower costs.

Original languageEnglish
Title of host publicationSmart Materials and Nondestructive Evaluation for Energy Systems 2016
EditorsNorbert G. Meyendorf, Theodoros E. Matikas, Kara J. Peters
PublisherSPIE
Number of pages15
Volume9806
ISBN (Electronic)9781510600478
DOIs
Publication statusPublished - Apr 2016
EventSmart Materials and Nondestructive Evaluation for Energy Systems 2016 - Las Vegas, United States
Duration: 21 Mar 201623 Mar 2016

Conference

ConferenceSmart Materials and Nondestructive Evaluation for Energy Systems 2016
CountryUnited States
CityLas Vegas
Period21/03/1623/03/16

Keywords

  • Damage detection
  • damage localization
  • damage severity estimation
  • fuzzy computing
  • neural networks
  • time series methods
  • vibration analysis
  • wind turbines

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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  • Cite this

    Hoell, S., & Omenzetter, P. (2016). Neuro-fuzzy computing for vibration-based damage localization and severity estimation in an experimental wind turbine blade with superimposed operational effects. In N. G. Meyendorf, T. E. Matikas, & K. J. Peters (Eds.), Smart Materials and Nondestructive Evaluation for Energy Systems 2016 (Vol. 9806). [98060J] SPIE. https://doi.org/10.1117/12.2218139