Abstract
Vibration-based structural health monitoring (VSHM) methodologies provide a robust, data-driven, system for damage diagnosis. However, there are a few challenges that are currently being investigated to ensure the systems are more reliable for decision-making. The features selected from the vibration responses are not only sensitive to damage but also to environmental and operational variations (EOV). This paper aims to investigate the use of a principal component analysis (PCA) based system for VSHM. In particular, the aim is to compare different approaches, using the same dataset, to explore the effect that data manipulation has on the damage detection capabilities of such a system when it is corrupted by EOV. The data that was used for this study was first taken from a simulated five degree of freedom spring-mass-damper system and secondly from an in-operation Vestas V27 wind turbine with damaged and undamaged scenarios. The simulated system was subjected to varying temperatures and involved four states; one healthy state followed by three states with increasing damage, represented by the reduction of a spring stiffness. Each combination of data manipulation was compared to determine their performance and limitations on removing EOV for reliable damage diagnosis.
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References
Garcia, D., Trendafilova, I.: A multivariate data analysis approach towards vibration analysis and vibration-based damage assessment: application for delamination detection in a composite beam. J. Sound Vib. 25(10), 7036–7050 (2014)
Dervilis, N., Worden, K., Cross, E.: On robust regression analysis as a means of exploring environmental and operational conditions for SHM data. J. Sound Vib. 347(7), 279–296 (2015)
Mujica, L., Rodellar, J., Fernandez, A., Guemes, A.: Q-statistic and T2-statistic PCA-based measures for damage assessment in structures. Struct. Health Monit. 10(5), 539–553 (2010)
Peeters, B., Maeck, J., De Roeck, G.: Vibration-based damage detection in civil engineering: excitation sources and temperature effects. Smart Mater. Struct. 10(3), 518–527 (2001)
Kojidi, S.M., Dohler, M., Bernal, D., Liu, Y.: Linear Projection Techniques in Damage Detection Under a Changing Environment, pp. 325–332. Springer, New York (2013)
Cross, E.J., Worden, K., Chen, Q.: Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data. Proc. R. Soc. 467(2133) (2011). https://doi.org/10.1098/rspa.2011.0023
Shi, H., Worden, K., Cross, E.J.: A regime-switching cointegration approach for removing environmental and operational variations in structural health monitoring. Mech. Syst. Signal Process. 103, 381–397 (2018)
Kim, K.I., Jung, K., Kim, H.J.: Face recognition using kernel principal component analysis. IEEE Signal Process. Lett. 9(2), 40–42 (2002)
Byrne, G., Crapper, P., Mayo, K.: Monitoring land-cover change by principal component analysis of multitemporal landset data. Remote Sens. Environ. 10(3), 175–184 (1980)
Yan, A., Kerschen, G., De Boe, P., Golinval, J.: Structural damage diagnosis under varying environmental conditions: part I: A linear analysis. Mech. Syst. Signal Process. 19(4), 847–864 (2005)
Wah, W.S., Chen, Y.-T., Roberts, G.W., Elamin, A.: Damage detection of structures subject to nonlinear effects of changing environmental conditions. Procedia Eng. 188, 248–255 (2017)
Ulriksen, M.D., Tcherniak, D., Damkilde, L.: Damage detection in an operating Vestas V27 wind. In: 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) Proceedings, Trento, IEEE (2015)
Gharibnezhad, F., Mujica, L., Rodellar, J., Fritzen, C.: Damage detection using principal component analysis based on wavelet ridges. Key Eng. Mater. 569, 916–923 (2013)
Park, S., Lee, J.-J., Yun, C.-B., Inman, D.J.: Electro-mechanical impedance-based wireless structural health monitoring using PCA-data compression and k-means clustering algorithms. J. Intell. Mater. Syst. Struct. 19(4), 509–520 (2007)
Tang, J.: Frequency response based damage detection using principal component analysis. In: 2005 IEEE International Conference on Information Acquisition, Hong Kong, IEEE (2006)
Gomez Gonzalez, A., Fassois, S.: A supervised vibration-based statistical methodology for damage detection under varying environmental conditions & its laboratory assessment with a scale wind turbine blade. J. Sound Vib. 366, 484–500 (2016)
Westerhuis, J.A., Kourti, T., MacGregor, J.F.: Comparing alternative approaches for multivariate statistical analysis of batch process data. Chemometrics 13(3–4), 397–413 (1999)
Tcherniak, D., Molgaard, L.: Active vibration-based structural health monitoring system for wind turbine blade: demonstration on an operating Vestas V27 wind turbine. Struct. Health Monit. 16(5), 536–550 (2017)
Cross, E., Manson, G., Worden, K., Pierce, S.: Features for damage detection with insensitivity to environmental and operational variations. Proc. R. Soc. A: Math. Phys. Eng. Sci. 467(2133), 4098–4122 (2012)
García, D., Tcherniak, D.: An experimental study on the data-driven structural health monitoring of large wind turbine blades using a single accelerometer and actuator. Mech. Syst. Signal Process. 127, 102–119 (2019, in press)
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The authors would like to acknowledge The Carnegie Trust for the Universities of Scotland for their support of this research project.
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Roberts, C., Garcia, D., Tcherniak, D. (2020). A Comparative Study on Data Manipulation in PCA-Based Structural Health Monitoring Systems for Removing Environmental and Operational Variations. In: Wahab, M. (eds) Proceedings of the 13th International Conference on Damage Assessment of Structures. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-8331-1_13
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DOI: https://doi.org/10.1007/978-981-13-8331-1_13
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