| Description |
Structural health monitoring systems collect and process large volumes of data taken over many years of a structure's service. Ultrasonic guided wave systems, in particular, must process an abundance of time-domain waveform data from widely distributed sensors. As few as 8~sensors that transmit and receive ultrasonic waves in pitch-catch mode every 10~minutes can accumulate over one terabyte of data in five to ten years. This number quickly rises as systems grow in size and complexity. As a result, computation and storage efficiency is extremely important, and current guided wave damage detection technologies cannot efficiently process such large data sets. This thesis starts with an introduction and survey of the structural health monitoring and data compression fields. A dimensionality reduction technique using random projections is proposed. The potential for dimensionality reduction method for improving computation time and storage efficiency is discussed. Random projections using sparse matrices is investigated as a tool in implementing a real-time structural health monitoring system with singular value decomposition as a damage detection method. At the end, future directions for research to make this technology more viable in application are suggested. |
