Abstract
Structural health monitoring (SHM) with a dense sensor network and repeated vibration measurements produces lots of data that have to be stored. If the sensor network is redundant, data compression is possible by storing the signals of selected Bayesian virtual sensors only, from which the omitted signals can be reconstructed with higher accuracy than the actual measurement. The selection of the virtual sensors for storage is done individually for each measurement based on the reconstruction accuracy. Data compression and reconstruction for SHM is the main novelty of this paper. The stored and reconstructed signals are used for damage detection and localization in the time domain using spatial or spatiotemporal correlation. Whitening transformation is applied to the training data to take the environmental or operational influences into account. The first principal component of the residuals is used to localize damage and also to design the extreme value statistics control chart for damage detection. The proposed method was studied with a numerical model of a frame structure with a dense accelerometer or strain sensor network. Only five acceleration or three strain signals out of the total 59 signals were stored. The stored and reconstructed data outperformed the raw measurement data in damage detection and localization.
Highlights
Structural health monitoring (SHM) is based on frequent vibration measurements using a sensor network with a large number of sensors
The main novelty of the present paper is to introduce a data compression technique so that the data compression ratio is large and the reconstructed data are so accurate that they can be applied to SHM
It is by no means claimed that the proposed method is optimal, but a single algorithm was selected to study whether data compression and reconstruction can be successfully applied to detect and localize damage in the time domain
Summary
Structural health monitoring (SHM) is based on frequent vibration measurements using a sensor network with a large number of sensors. The objective of the present study is to detect and localize damage using the stored and reconstructed virtual sensor data. Time-domain and feature-domain methods for damage detection were compared [22], and it was found that the selected features were more sensitive to damage than the physical or virtual sensor data. It is by no means claimed that the proposed method is optimal, but a single algorithm was selected to study whether data compression and reconstruction can be successfully applied to detect and localize damage in the time domain. Feature-domain methods are beyond the scope of this paper, because many feature-extraction techniques already include noise reduction due to averaging and may not benefit from the proposed data compression and reconstruction.
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