Abstract
Structural Health Monitoring (SHM) is an important technique used to preserve many types of structures in the short and long run, using sensor networks to continuously gather the desired data. However, this causes a strong impact in the data size to be stored and processed. A common solution to this is using compression algorithms, where the level of data compression should be adequate enough to allow the correct damage identification. In this work, we use the data sets from a laboratory three-story structure to evaluate the performance of common compression algorithms which, then, are combined with damage detection algorithms used in SHM. We also analyze how the use of Independent Component Analysis, a common technique to reduce noise in raw data, can assist the detection performance. The results showed that Piecewise Linear Histogram combined with Nonlinear PCA have the best trade-off between compression and detection for small error thresholds while Adaptive PCA with Principal Component Analysis perform better with higher values.
Highlights
Structural Health Monitoring (SHM) aims to identify damages in a structure to execute the needed measures for prevention and correction
We use the data sets from a laboratory three-story structure to evaluate the performance of common compression algorithms which, are combined with damage detection algorithms used in SHM
The results showed that Piecewise Linear Histogram combined with Nonlinear PCA have the best trade-off between compression and detection for small error thresholds while Adaptive PCA with Principal Component Analysis perform better with higher values
Summary
Structural Health Monitoring (SHM) aims to identify damages in a structure to execute the needed measures for prevention and correction. This process involves the structure observation over time using dynamic response. 2016 129 measures with periodical intervals [1] This is done by using electronic sensors, suitable to the parameter (e.g., acceleration, strain and temperature) that must be monitored. Structures are subject to environmental and operational influences, such as varying temperature, moisture and loading conditions. All of these effects may impact the performance of a damage classifier, which can signal a damage because of changes in the environment when, in reality, there is none [3]. It may be possible that substantial overloads occur on the network and on the measurement database [5]
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