Incipient Damage Detection for Large Area Structures Monitored With a Network of Soft Elastomeric Capacitors Using Relative Entropy

Structural health monitoring of mesoscale structures is difficult due to their large sizes and often complex geometries. A solution to this challenge lies in the development of sensing skins. Sensing skins are an emerging technology that enables a broad range of sensors and their associated electronics to be integrated onto a single sheet, therefore, reducing the cost and complexity associated with deploying these dense sensor networks onto mesoscale structures. This paper presents a new algorithm for the detection and localization of incipient damage in structures. The algorithm is specialized for a sensing skin consisting of a large area electronic termed as soft elastomeric capacitor. The proposed algorithm utilizes relative entropy to quantify the dissimilarity between one sensor and every other sensor in the network, with more weight placed on the dissimilarities between the sensor of interest and those in its immediate vicinity. The algorithm is data-driven and does not require the healthy condition be known or historical data sets be available to generate damage sensitive indexes. Numerical simulations are used to demonstrate the effectiveness of the data-driven algorithm in both detecting and localizing incipient damage.