Digital Structural Health Monitoring for SHM Design

Abstract

Structural Health Monitoring (SHM) technology is gaining more and more acceptance in world-wide industries as a new innovative approach for providing information regarding the state of health of structural platforms in real-time or ondemand through distributed sensor networks built onto the structures. The main challenge, however, associated with SHM is that sensors used in the process are permanently mounted on the structure; therefore, it is desirable to have the optimal sensor location to provide the highest SHM performance before deploying the SHM system for real time monitoring of the structures. Digital Structural health monitoring (DGSHM) is a new concept which utilizes simulations instead of experimentation to characterize the performance of SHM systems with respect to various parameters that may affect the results of the SHM predictions. Therefore, the implementation of the concept requires the involvement of three components: 1) A physical model that can produce sensor signals from simulating structural responses, 2) An SHM diagnostic system that can take simulated data and predict the performance of the SHM system, and 3) A sensitivity analysis that would optimize the performance of the proposed SHM system. In this study, an acousto-ultrasound based SHM system is considered. The effectiveness of the proposed framework is experimentally assessed via its application to an aluminum coupon, with three distinct sensor network configurations. The diagnostics results for all three cases are extracted and are used to quantify the sensitivity of the sensor network in terms of prediction error.