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Shock of Vibration-based Technologies, Part II: Detection

M. K. HOVGAARD, J. B. HANSEN, A. SKAFTE, P. OLSEN, R. BRINCKER

Abstract


Several different approaches to structural damage detection are compared in a study of both numerically simulated and experimental data, acquired from trials in the laboratory. Structural damage detection is decision making under uncertainty and is the process of discriminating a data point of a selected feature vector from the reference population of the feature vector in the undamaged state. In the study four types of parametric features, all linked to modal properties, are investigated. At the same time, four discrimination algorithms from the field of unsupervised machine learning are applied and compared using detection theory metrics. The study attempts to clarify how global information of mode shapes and eigenfrequencies compare to a simpler scalar time-series model. To compare the feature models, four types of discrimination algorithms from the unsupervised machine learning regime were applied. A simulation study and an experimental validation were carried out and the results presented. The study shows that both the choice of feature model and the choice of discriminant algorithm are important to damage detection. Furthermore, the increased performance of the sensor-array models over a single-sensor model was shown.

doi: 10.12783/SHM2015/183


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