This paper explores the use of unsupervised neural computation for event detection (ED) in structural health monitoring (SHM) systems. ED techniques are useful in SHM systems for minimizing the size of SHM data sets, and the costs associated with analyzing, transmitting and storing SHM data. The approach to ED explored here is adaptive, self-configuring and does not require detailed information about the structure being monitored.
A neural network approach known as frequency sensitive competitive learning (FSCL) is used to model the sensor output of an SHM system. SHM system output states which disagree with the model learned are deemed "novel" and detected as SHM events.
The FSCL-ED system is evaluated with SHM data from three structures including the Taylor Bridge, the Portage Creek Bridge and the Golden Boy Statue. Furthermore, this system is able to identify strain gauge events of 0.75, 12.5, 1.25 microstrain or smaller in the SHM measurement data from the Taylor Bridge, the Portage Creek Bridge, and the Golden Boy respectively. The FSCL-ED system is able to identify accelerometer events of .0045g, 0.0020g or smaller in the SHM measurement data from the Portage Creek Bridge, and the Golden Boy respectively.
The FSCL-ED system is compared to a simplified event detection (S-ED) system, which does not use power spectral density estimation or unsupervised neural computation. The S-ED system is shown to be effective but less sensitive than the FSCL-ED system to SHM events. As well, the FSCL-ED system is better able to adapt to noisy environments.