An Echo State Network Approach to Structural Health Monitoring

Wootton, AJ; Day, CR; Haycock, PW

Abstract

Echo State Networks (ESNs) have been applied to time-series data arising from a structural health monitoring multi-sensor array placed onto a test footbridge which has been subjected to a number of potentially damaging interventions over a three year period. The time-series data, sampled approximately every five minutes from ten temperature sensors, have been used as inputs and the ESNs were tasked with predicting the expected output signal from eight tilt sensors that were also placed on the footbridge. The networks were trained using temperature and tilt sensor data up to the first intervention and subsequent discrepancies in the ESNs' prediction accuracy allowed inferences to be made about when further interventions occurred and also the level of damage caused. Comparing the error in signals with the location of each of the tilt sensors allowed damaged regions to be determined.

Citation

Wootton, A., Day, C., & Haycock, P. (2015, July). An Echo State Network Approach to Structural Health Monitoring. Presented at International Joint Conference On Neural Networks 2015, Killarney

• Presentation Conference Type: Conference Paper (published)
• Conference Name: International Joint Conference On Neural Networks 2015
• Start Date: Jul 12, 2015
• End Date: Jul 17, 2015
• Acceptance Date: Jul 12, 2015
• Publication Date: Jul 12, 2015
• Publicly Available Date: May 26, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Book Title: 2015 International Joint Conference on Neural Networks (IJCNN)
• ISBN: 978-1-4799-1959-8
• DOI: https://doi.org/10.1109/IJCNN.2015.7280627
• Keywords: Structural Health Monitoring, Echo State Networks, Reservoir Computing Applications, Wireless Sensor Networks
• Public URL: https://keele-repository.worktribe.com/output/406248
• Publisher URL: http://dx.doi.org/10.1109/IJCNN.2015.7280627