Abstract
In this paper, results from on-going studies for extending a damage detection approach previously developed by the authors are presented. The methodology in the previous form was able to detect and locate the damage/anomaly successfully by using free response acceleration data employing a sensor clustering based time series modeling approach. The basic idea behind the methodology is that an Auto-Regressive model with eXogenous input (ARX model) between the outputs of a structure can be related to the structural properties of the system without using the input (excitation to the structure) or any other information. The improved version can detect, locate and quantify mass, stiffness and damping changes separately in a numerical model by using acceleration, velocity and displacement data. Based on the results, the potential and advantages of the methodology under investigation are discussed. Its limitations and shortcomings in the current version are also addressed along with proposed solutions and future work plans.
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© 2013 The Society for Experimental Mechanics, Inc.
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Mei, Q., Gul, M. (2013). An Improved Methodology for Anomaly Detection Based on Time Series Modeling. In: Catbas, F., Pakzad, S., Racic, V., Pavic, A., Reynolds, P. (eds) Topics in Dynamics of Civil Structures, Volume 4. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6555-3_30
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DOI: https://doi.org/10.1007/978-1-4614-6555-3_30
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