This paper explores the use of unsupervised neural networks and frequency sensitive competitive learning for novel event identification in structural health monitoring (SHM) systems. Our approach assigns a novelty metric based upon the output states of an SHM system. The technique can be applied in data decimation schemes, to enhance the monitoring of such systems, and as an aide to SHM data analysis. Learning units provide a means of characterizing an SHM system, and are subsequently used to assign a novelty metric to new SHM data. The system has been evaluated using data from the Taylor Bridge and Golden Boy statue in Winnipeg, Canada and the Portage Creek bridge in Victoria, Canada. The system is capable of analyzing SHM data from a 14-channel system, recording data at 32 Hz, using 32 learning units at approximately 30 times real-time on an AMD AthlonXP 2500+ based computer. The event identification system is most sensitive to SHM data which exhibits unusual power spectra, including data which shows abrupt changes in sensor outputs. The system may be cascaded in order to perform basic classification of events after identification.
