Damage Detection Using Two-Stage Compressive Sensing

NASA is developing new vehicles for human space flight. The Orion crew module is a reusable capsule that provides a habitat for the crew and facilitates exploration of the Moon, asteroids, and Mars. The Space Launch System (SLS) is a heavy lift system and is part of NASArsquo;s deep space exploration plans. The SLS will carry humans beyond low Earth orbit and will deliver elements of the Lunar Orbital Platform-Gateway. Many of these spacecraft are targeted for long-term use, which offers challenges for inspection and maintenance. In orbit or on the Moon, the use of traditional NDE is prohibitive because of location and inaccessibility, and infrequent inspection can lead to conservative, high-weight designs. NASA is seeking technologies to facilitate inspections on large complex structures and provide reliable assessments of structural health.nbsp;Structural health monitoring (SHM) can help overcome inspection difficulties and has shown good results on small structures. However, transition to large complex structures has been slow. Some reasons for the slow adoption are difficulties with large sensor arrays, timely analysis of large data sets, and overall weight of the system. In order to realize the benefits of SHM, therersquo;s a need to reduce the number of sensors and minimize data acquisition processes while maintaining the ability to accurately detect, locate, and characterize damage.nbsp;Compressive Sensing (CS) has been shown to greatly reduce data acquisition and processing burdens by providing mathematical guarantees for accurate signal recovery from far fewer samples than conventionally needed. In this project, it is proposed to develop an ultrasound software package to detect damage in large complex structures using CS at two stages in the data acquisition and analysis process: (1) temporally undersampled sensor signals from (2) spatially undersampled sensor arrays, resulting in faster data acquisition and reduced data sets without any loss in damage detection ability.nbsp;