205 Digital Image Correlation  for Performance Monitoring  

Miguel Palaviccini*, Dan Turner*  and Michael Herzberg** 

Abstract  

Evaluating the health of a mechanism  requires more than just a binary evaluation of whether an operation  was completed. I t requires analyzing more comprehensive, full -field data.  Health monitoring is a process  of non- destructively identifying characteristics that indicate  the fitness of an engineered component. In  order to monitor unit health in a production setting, a n automated test system must be  created to capture  the motion of mechanism  parts  in a real -time and non- intrusive manner . One way to accomplish this is by   using high- speed video and Digital Image Correlation (DIC) . In this approach, i ndividual frames of the  video are analyzed to track the motion of mechanism components. The derived performance metrics  allow for  state -of-health monitoring and improved fidelity of mechanism modeling.  The results are  in-situ  state -of-health identification and performance prediction.  This paper  introduces  basic concepts of this test  method,  and discuss es two main themes:   the use of laser marking to add fiducial patterns to mechanism  components, and new software developed to track objects wit h complex shapes, even as they move  behind obstructions.  Finally , the implementation of these tests into an automated tester is  discussed.  

Introduction  

The underlying premise of component health monitoring is to c haracterize the baseline performance of a  unit, allowing for future tests to help identify  any mechanical damage caused by environmental loading,  as well as future performance of the component as that damage accumulates. In a production setting, the monitoring must be done in quasi real -time,  with an automated system that is able to diagnose  component fidelity with minimal to no human interaction. The current application focuses  on using optical  measurements coupled with digital image correlation  (DIC)  for health monitoring, as opposed to wave   propagation, thermal, or other measurement variables that could also be indicative of component health.    The basic process and nomenclature of DIC is given in Ref 1. Displacements are determined by  comparing an image of the deformed object to a reference image. Traditionally a square subset is used  as the template to match between the reference and deformed image. I n most  cases, fiducials  (the  contrast of light and dark within the image)  must be applied to the object of interest in a random fashion.  The s et of fiducials on any given part is often referred to as the speckle pattern.  

Square subsets, while mathematically simple, can be challenging to fit onto irregularly shaped mechanism components. Conformal subsets, on the other hand, enable the entirety of the object being  tracked to be included in the subset, increasing the information available for tracking and thus increasing  accuracy. As a bonus, conformal subsets also enhance the tracking of objects that are partially obscured.  This paper briefly dis cusses the advantages of the newly developed DIC code by Sandia National  Laboratories, and how it is currently being used to help extract state of health information from components.  

For traditional DIC applications , these random fiducial patterns  are often applied using a light dusting of  spray paint to the product. I n mechanisms work, this can be problematic. When marking a mechanism  that is to be fielded, any method that adds organic material  or moisture, or alters the shape and mass of  the component sh ould be avoided. Additionally, t he paint method provides poor control of speckle size                                                              * Sandia National Laboratories, Albuquerque, NM   ** National Security Campus, Kansas City, MO  

Proceedings of the 43rd Aerospace Mechanisms Symposium, NASA Ames Research Center, May 4-6, 2016