Sequential projection pursuit for optimal transformation of autoregressive coefficients for damage detection in an experimental wind turbine blade

Simon Hoell, Piotr Omenzetter

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Abstract

The performance, and with it, the utility of structural health monitoring systems depends strongly on the efficiency of damage sensitive features (DSFs) for describing the state of a structure. Several approaches are available for extracting DSFs from acceleration response signals, but they are often high dimensional. This affects significantly data processing and storage demands. Therefore, reducing DSF dimensions while maintaining or even improving damage detectability is desired. The present study explores the use of sequential projection pursuit for identifying low-dimensional DSF transformations optimized for structural damage detection. Here, transformation vectors are obtained sequentially using an advanced evolutionary optimization technique. A statistical objective function is employed to facilitate making decisions about the structural state with the help of statistical hypothesis testing. Optimal numbers of transformation vectors are found by fast forward selection. The approach is demonstrated using initial DSFs defined as autoregressive coefficients from acceleration response signals of an experimental wind turbine blade. Wind-like excitations were applied with the help of a pedestal fan, and damages were simulated non-destructively by adding small masses. The results demonstrate that the proposed methodology can considerably reduce DSF dimensionalities without deteriorating the damage detection performance. Conversely, the detectability of some damages could be improved in comparison to using selected original DSFs. This is promising for future developments of efficient vibration-based structural health monitoring methods.

Original languageEnglish
Pages (from-to)2226-2231
Number of pages6
JournalProcedia Engineering
Volume199
DOIs
Publication statusPublished - 12 Sep 2017
EventX International Conference on Structural Dynamics, EURODYN 2017 - Faculty of Civil and Industrial Engineering of Sapienza University of Rome Italy, Rome, Italy
Duration: 10 Sep 201713 Sep 2017
http://Faculty of Civil and Industrial Engineering of Sapienza University of Rome Italy

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Damage detection
Structural health monitoring
Wind turbines
Turbomachine blades
Fans
Decision making
Testing

Keywords

  • Damage detection
  • Projection pursuit
  • Wind turbines
  • Time series methods
  • Autoregressive models
  • Statistical hypothesis testing

Cite this

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title = "Sequential projection pursuit for optimal transformation of autoregressive coefficients for damage detection in an experimental wind turbine blade",
abstract = "The performance, and with it, the utility of structural health monitoring systems depends strongly on the efficiency of damage sensitive features (DSFs) for describing the state of a structure. Several approaches are available for extracting DSFs from acceleration response signals, but they are often high dimensional. This affects significantly data processing and storage demands. Therefore, reducing DSF dimensions while maintaining or even improving damage detectability is desired. The present study explores the use of sequential projection pursuit for identifying low-dimensional DSF transformations optimized for structural damage detection. Here, transformation vectors are obtained sequentially using an advanced evolutionary optimization technique. A statistical objective function is employed to facilitate making decisions about the structural state with the help of statistical hypothesis testing. Optimal numbers of transformation vectors are found by fast forward selection. The approach is demonstrated using initial DSFs defined as autoregressive coefficients from acceleration response signals of an experimental wind turbine blade. Wind-like excitations were applied with the help of a pedestal fan, and damages were simulated non-destructively by adding small masses. The results demonstrate that the proposed methodology can considerably reduce DSF dimensionalities without deteriorating the damage detection performance. Conversely, the detectability of some damages could be improved in comparison to using selected original DSFs. This is promising for future developments of efficient vibration-based structural health monitoring methods.",
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author = "Simon Hoell and Piotr Omenzetter",
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AB - The performance, and with it, the utility of structural health monitoring systems depends strongly on the efficiency of damage sensitive features (DSFs) for describing the state of a structure. Several approaches are available for extracting DSFs from acceleration response signals, but they are often high dimensional. This affects significantly data processing and storage demands. Therefore, reducing DSF dimensions while maintaining or even improving damage detectability is desired. The present study explores the use of sequential projection pursuit for identifying low-dimensional DSF transformations optimized for structural damage detection. Here, transformation vectors are obtained sequentially using an advanced evolutionary optimization technique. A statistical objective function is employed to facilitate making decisions about the structural state with the help of statistical hypothesis testing. Optimal numbers of transformation vectors are found by fast forward selection. The approach is demonstrated using initial DSFs defined as autoregressive coefficients from acceleration response signals of an experimental wind turbine blade. Wind-like excitations were applied with the help of a pedestal fan, and damages were simulated non-destructively by adding small masses. The results demonstrate that the proposed methodology can considerably reduce DSF dimensionalities without deteriorating the damage detection performance. Conversely, the detectability of some damages could be improved in comparison to using selected original DSFs. This is promising for future developments of efficient vibration-based structural health monitoring methods.

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