Structural Health Monitoring of Composite Material Typical of Wind Turbine Blades by Novelty Detection on Vibration Response

Abstract:

New generations of offshore wind turbines are playing a leading role in the energy arena. One of the target challenges is to achieve reliable Structural Health Monitoring (SHM) of the blades. Fault detection at the early stage is a vital issue for the structural and economical success of the large wind turbines. In this study, experimental measurements of Frequency Response Functions (FRFs) are used and identification of mode shapes and natural frequencies is accomplished via an LMS system. Novelty detection is introduced as a robust statistical method for low-level damage detection which has not yet been widely used in SHM of composite blades. Fault diagnosis of wind turbine blades is a challenge due to their composite material, dimensions, aerodynamic nature and environmental conditions. The novelty approach combined with vibration measurements introduces an online condition monitoring method. This paper presents the outcomes of a scheme for damage detection of carbon fibre material in which novelty detection approaches are applied to FRF measurements. The approach is demonstrated for a stiffened composite plate subject to incremental levels of impact damage.

• Periodical: Key Engineering Materials (Volume 518)
• Pages: 319-327
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.518.319
• Online since: July 2012
• Authors: Nikolaos Dervilis, R. Barthorpe, Wieslaw Jerzy Staszewski, Keith Worden
• Keywords: Carbon Fibre Composites, Damage Detection, Novelty Analysis, Outlier Algorithm

References

[1] B. D. Agarwal, L. J. Broutman and K. Chandrashekhara, Analysis and performance of fibre composites, Wiley, (2006).

[2] A. Deraemaeker, K. Worden, New trends in vibration based structural health monitoring, CISM Courses and Lectures, Vol. 520, (2010), Springer-Verlag.

[3] K. Worden, G. Manson, N. R. Fieller, Damage detection using outlier analysis, Journal of Sound and Vibration, 229 (1999) 647–667.

DOI: 10.1006/jsvi.1999.2514

[4] P. Filzmoser, A multivariate outlier detection method, Proceedings of the 7th International Conference on Computer Data Analysis and Modeling, Minsk, Belarus, (2004) 18-22.

[5] N. M. M. Maia, J. M. M Silva, Theoretical and experimental modal analysis, Research Studies Press, (1998).

[6] G. Manson, K. Worden, D. J. Allman. Experimental validation of a structural health monitoring methodology: Part III. Novelty detection on an aircraft wing, Journal of Sound and Vibration, 259 (2003) 345–363.

DOI: 10.1006/jsvi.2002.5167

[7] G. Manson, K. Worden, D. J. Allman, Experimental validation of a structural health monitoring methodology: Part II. Novelty detection on an aircraft wing, Journal of Sound and Vibration. 259 (2003) 345–363.

DOI: 10.1006/jsvi.2002.5167

[8] K. Worden, G. Manson, D. J. Allman Experimental validation of a structural health monitoring methodology: Part I. Novelty detection on a laboratory structure, Journal of Sound and Vibration, 259 (2003) 323–343.

DOI: 10.1006/jsvi.2002.5168

[9] C. M. Bishop, Neural networks for pattern recognition, Oxford University Press, (1995).

[10] C. M. Bishop, Pattern recognition and machine learning, Springer Press, (2006).