期刊
JOURNAL OF SOUND AND VIBRATION
卷 259, 期 2, 页码 323-343出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1006/jsvi.2002.5168
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This paper is concerned with the experimental validation of a structural health monitoring methodology, previously only investigated using synthetic data. The structure considered here is a simplified model of a metallic aircraft wingbox i.e., a plate incorporating stiffening elements. Damage is simulated by a saw-cut to one of the panel stringers (stiffeners). The analysis approach uses novelty detection based on measured transmissibilities from the structure. Three different novelty detection algorithms are considered here: outlier analysis, density estimation and an auto-associative neural network technique. All three methods are shown to be successful to an extent, although a critical comparison indicates reservations about the density estimation approach when used on sparse data sets. (C) 2002 Elsevier Science Ltd. All rights reserved.
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