In-situ damage localization for a wind turbine blade through outlier analysis of stochastic dynamic damage location vector-induced stress resultants

Today, structural integrity inspections of wind turbine blades are typically carried out by the use of rope or platform access. Since these inspection approaches are both tedious and extremely costly, a need for a method facilitating reliable, remote monitoring of the blades has been identified. In this article, it is examined whether a vibration-based damage localization approach proposed by the authors can provide such reliable monitoring of the location of a structural damage in a wind turbine blade. The blade, which is analyzed in idle condition, is subjected to unmeasured hits from a mounted actuator, yielding vibrations that are measured with a total of 12 accelerometers; of which 11 are used for damage localization. The employed damage localization method is an extended version of the stochastic dynamic damage location vector method, which, in its origin, is a model-based method that interrogates damage-induced changes in a surrogate of the transfer matrix. The surrogate’s quasi-null vector associated with the lowest singular value is converted into a pseudo-load vector and applied to a numerical model of the healthy structure in question, hereby, theoretically, yielding characteristic stress resultants approaching zero in the damaged elements. The proposed extension is based on outlier analysis of the characteristic stress resultants to discriminate between damaged elements and healthy ones; a procedure that previously, in the context of experiments with a small-scale blade, has proved to mitigate noise-induced anomalies and systematic, non-damage-associated adverse effects.

[1]  Keith Worden,et al.  Features for damage detection with insensitivity to environmental and operational variations , 2012, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[2]  Poul Henning Kirkegaard,et al.  Operational modal analysis and wavelet transformation for damage identification in wind turbine blades , 2016 .

[3]  Dmitri Tcherniak,et al.  Damage localization in a residential-sized wind turbine blade by use of the SDDLV method , 2015 .

[4]  Dionisio Bernal,et al.  Load Vectors for Damage Location in Systems Identified from Operational Loads , 2010 .

[5]  Laurent Mevel,et al.  Robust statistical damage localization with stochastic load vectors , 2015 .

[6]  Randall J. Allemang,et al.  A Correlation Coefficient for Modal Vector Analysis , 1982 .

[7]  Bart De Moor,et al.  Subspace Identification for Linear Systems: Theory ― Implementation ― Applications , 2011 .

[8]  Huei-Huang Lee,et al.  Finite element simulations with ansys workbench 15 , 2014 .

[9]  Poul Henning Kirkegaard,et al.  On Structural Health Monitoring of Wind Turbine Blades , 2013 .

[10]  Dmitri Tcherniak,et al.  Vibration-based SHM System: Application to Wind Turbine Blades , 2015 .

[11]  Laurent Mevel,et al.  Damage Detection in Wind Turbine Blade Panels Using Three Different SHM Techniques , 2011 .

[12]  Spilios D. Fassois,et al.  Natural vibration response based damage detection for an operating wind turbine via Random Coefficient Linear Parameter Varying AR modelling , 2015 .

[13]  Jung-Ryul Lee,et al.  Structural health monitoring for a wind turbine system: a review of damage detection methods , 2008 .

[14]  Daniele Zonta,et al.  Structural health monitoring of wind towers: remote damage detection using strain sensors , 2011 .

[15]  Gunner Chr. Larsen,et al.  Comparative study of OMA applied to experimental and simulated data from an operating Vestas V27 wind turbine , 2015 .

[16]  J. Magnus,et al.  Matrix Differential Calculus with Applications in Statistics and Econometrics , 1991 .

[17]  Martin Dalgaard Ulriksen,et al.  Structural Damage Localization by Outlier Analysis of Signal-processed Mode Shapes: Analytical and Experimental Validation , 2016 .

[18]  Dmitri Tcherniak,et al.  Statistical evaluation of characteristic SDDLV-induced stress resultants to discriminate between undamaged and damaged elements , 2015 .

[19]  Dmitri Tcherniak,et al.  Damage detection in an operating Vestas V27 wind turbine blade by use of outlier analysis , 2015, 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) Proceedings.

[20]  R. Fox,et al.  Rates of change of eigenvalues and eigenvectors. , 1968 .

[21]  Jonathan White,et al.  Structural health and prognostics management for the enhancement of offshore wind turbine operations and maintenance strategies , 2014 .

[22]  M.T. Iqbal,et al.  Reliability and condition monitoring of a wind turbine , 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

[23]  Gunner Chr. Larsen,et al.  Effect of a Damage to Modal Parameters of a Wind Turbine Blade , 2014 .

[24]  Marek Krawczuk,et al.  Damage detection in turbine wind blades by vibration based methods , 2009 .

[25]  Malcolm McGugan Application for Wind Turbine Blades , 2013 .

[26]  Dmitri Tcherniak,et al.  Application of OMA to an Operating Wind Turbine: now including Vibration Data from the Blades , 2013 .

[27]  Morten Hartvig Hansen,et al.  Improved Modal Dynamics of Wind Turbines to Avoid Stall‐induced Vibrations , 2003 .

[28]  Dionisio Bernal,et al.  Damage Localization from the Null Space of Changes in the Transfer Matrix , 2006 .

[29]  David Cloutier,et al.  Artificial and Natural Excitation Testing of SWiFT Vestas V27 Wind Turbines , 2014 .

[30]  S. Fassois,et al.  Vibration – Based Statistical Damage Detection For Scale Wind Turbine Blades Under Varying Environmental Conditions , 2013 .

[31]  Matthew S. Allen,et al.  Modal Analysis of Rotating Wind Turbine Using Multiblade Coordinate Transformation and Harmonic Power Spectrum , 2014 .

[32]  Piotr Omenzetter,et al.  Experimental damage detection in a wind turbine blade model using principal components of response correlation functions , 2015 .

[33]  Subhash Sharma Applied multivariate techniques , 1995 .