PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Feature extraction and selection from vibration measurements for structural health monitoring
Janne Toivola and Jaakko Hollmen
Proceedings of the 8th International Symposium on Intelligent Data Analysis Volume 5772 of Lecture Notes in Computer Science, pp. 213-224, 2009. ISSN 0302-9743

Abstract

Structural Health Monitoring (SHM) aims at monitoring buildings or other structures and assessing their condition, alerting about new defects in the structure when necessary. For instance, vibration measurements can be used for monitoring the condition of a bridge. We investigate the problem of extracting features from lightweight wireless acceleration sensors. On-line algorithms for frequency domain monitoring are considered, and the resulting features are combined to form a large bank of candidate features. We explore the feature space by selecting random sets of features and estimating probabilistic classifiers for damage detection purposes. We assess the relevance of the features in a large population of classifiers. The methods are assessed with real-life data from a wooden bridge model, where structural problems are simulated with small added weights.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6264
Deposited By:Jaakko Hollmen
Deposited On:08 March 2010