Digital Image Correlation for Performance Monitoring

摘要

Evaluating the health of a mechanism requires more than just a binary evaluation of whether an operationrnwas completed. It requires analyzing more comprehensive, full-field data. Health monitoring is a processrnof non-destructively identifying characteristics that indicate the fitness of an engineered component. Inrnorder to monitor unit health in a production setting, an automated test system must be created to capturernthe motion of mechanism parts in a real-time and non-intrusive manner. One way to accomplish this is byrnusing high-speed video and Digital Image Correlation (DIC). In this approach, individual frames of thernvideo are analyzed to track the motion of mechanism components. The derived performance metricsrnallow for state-of-health monitoring and improved fidelity of mechanism modeling. The results are in-siturnstate-of-health identification and performance prediction. This paper introduces basic concepts of this testrnmethod, and discusses two main themes: the use of laser marking to add fiducial patterns to mechanismrncomponents, and new software developed to track objects with complex shapes, even as they movernbehind obstructions. Finally, the implementation of these tests into an automated tester is discussed.