Abstract: Described herein are Compressive Sensing algorithms developed for automated reduction of NDE/SHM data from pitch-catch ultrasonic guided waves as well as a methodology using Compressive Sensing at two stages in the data acquisition and analysis process to detect damage: (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.

Abstract: The disclosure deals with a system and method for Compressive Sensing, which 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. A generalized Compressive Sensing methodology developed for automated reduction of non-destructive evaluation/structural health monitoring (NDE/SHM) data is effective for multiple types of NDE/SHM systems. The methodology uses Compressive Sensing at two stages in the data acquisition and analysis process to detect damage. The two stages are: (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. In addition, a graphical user interface helps guide and visualize the associated data reconstruction process.

Abstract: Described herein are Compressive Sensing algorithms developed for automated reduction of NDE/SHM data from pitch-catch ultrasonic guided waves as well as a methodology using Compressive Sensing at two stages in the data acquisition and analysis process to detect damage: (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.

Abstract: Described herein are Compressive Sensing algorithms developed for automated reduction of NDE/SHM data from pitch-catch ultrasonic guided waves as well as a methodology using Compressive Sensing at two stages in the data acquisition and analysis process to detect damage: (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.

Abstract: Described herein are Compressive Sensing algorithms developed for automated reduction of NDE/SHM data from pitch-catch ultrasonic guided waves as well as a methodology using Compressive Sensing at two stages in the data acquisition and analysis process to detect damage: (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.