Publication: Publication Date: 1 May 2020
Computational Framework for Dense Sensor Network Evaluation Based on Model-Assisted Probability of Detection
Recent sensing advances empower the deployment of dense sensor networks (DSNs) that can be used in automating the condition assessment process of large-scale structural and mechanical systems. To fully enable DSN technologies, it is critical to develop and implement co-design techniques that allow for the evaluation of their performance to attain the condition assessment targets. In this paper, we propose a technique to validate the design of DSNs. The technique consists of constructing a physical surrogate of the DSN equipped component based on the sensor configuration, updating the physical surrogate using the sliding mode theory based on generated or collected sensor data, and quantifying the performance of the DSN based on model-assisted probability of detection theory. The proposed technique is numerically verified and validated on a cantilever beam subjected to damage at its root and equipped with a network of soft elastomeric capacitors measuring strain. Various uncertainties are considered in the simulated system. The verification consists of confirming the capability of the adaptive process for the surrogate model at reaching an accurate representation of the full system. The validation consists of ranking the performance of various DSN configuration and benchmarking results against those obtained from the full finite element model. Results show that the proposed technique can be used to evaluate the performance of DSN configurations, but that the damage thresholds used in determining damage need to be standardized for successful field applications.
DOI: doi.org/10.32548/2020.me-04111
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