NDLI: Novelty detection by nonlinear factor analysis for structural health monitoring

Content Provider	IEEE Xplore Digital Library
Author	Lämsä, V. Raiko, T.
Copyright Year	2010
Description	Author affiliation: Aalto University, School of Science and Technology, Dep. of Applied Mechanics, P.O. Box 14300, 00076, Finland (Lämsä, V.) \|\| Aalto University, School of Science and Technology, Dep. of Information and Computer Science, P.O. Box 15400, 00076, Finland (Raiko, T.)
Abstract	In vibration-based structural health monitoring damage in structure is tried to detect from damage-sensitive features. Because neither prior information nor data about expected damage are normally available, damage detection problem must be solved by using a novelty detection approach. Features, which are sensitive to damage, are often sensitive to environmental and operational variations. Therefore elimination of these variations is essential for reliable damage detection. At present many of the damage detection methods are linear, though it has been shown that many of the vibration changes in structures are bilinear or nonlinear. This paper proposes to use nonlinear factor analysis to detect damage via elimination of external effects from damage features. The effectiveness of the proposed method is demonstrated by analyzing the experimental Z24 Bridge data with a comparison to a linear method. It is shown that elimination of adverse effects and damage detection are feasible.
Starting Page	468
Ending Page	473
File Size	577286
Page Count	6
File Format	PDF
ISBN	9781424478750
ISSN	15512541
e-ISBN	9781424478774
e-ISBN	9781424478767
DOI	10.1109/MLSP.2010.5588688
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-29
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bridges Monitoring Temperature measurement Feature extraction Mathematical model Data models Training data
Content Type	Text
Resource Type	Article

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