Feature Extraction and Selection from Vibration Measurements for Structural Health Monitoring

摘要

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.