210 Implementation into an automated tester environment   As previously mentioned, to monitor unit health in a production s etting, an automated test system must be  created.  A system was developed to capture a video of a mechanism in operation, process  the video to  extract two- dimensional motion information and r eport  results in an automated manner . This provides a  statement of  health prediction with a high confidence level. An implementation of the system has been in  operation for several years undergoing development, and the lessons learned are currently being  implemented into a qualified tester.    The tester has four main components. The first is a high speed video camera. For this application,  a  Phantom v1210 from Vision Research along with a Canon 100- mm f/2.8 macro lens allowed for  maximizing field of view within a compact space. Note that camera and lens selection are dependent on mechanism size and motion rate. The second component is a lighting system. As with most vision solutions, the idea is to o btain as much light as possible without casting harsh shadows or creating glare  off the surface of the tracked parts. The third component is a signal generator for providing the  mechanism drive signals and test start trigger. Finally, a PC to allow sequenc ing of the test steps and for  use to analyze the data as the test is run. The initial test setup was coded in LabVIEW, calling MATLAB  scripts for data analysis. To speed execution and reduce maintenance, the code is now in procedural  languages.    The basic order of operations is to activate the camera with a rolling frame buffer, feeding it the appropriate resolution, framerate, and exposure settings. The mechanism is then operated, with a trigger signal sent to the camera to begin saving data at the same ti me the mechanism receives its first drive  signal.  After the unit is operated, and the video has been captured, the frames are moved from the  camera to the data storage folder on the computer. The computer then calls the analysis script, which feeds the vid eo frames and predefined regions of interest to the tracking algorithm. The algorithm outputs  2-D position coordinates and angle for each ROI  during each frame. These traces are then analyzed to  extract time and duration of various events in the mechanism.   

Conclusions  and Future Work  

In summary, this paper describes and improved method for analyzing mechanism performance and provides lessons learned when applying DIC to extract useful state of health metrics from components. Specifically, the paper outlines robust and repeatable methods of marking fiducials on parts of interest as well as newly developed ways of extracting information from moving parts using conformal subsets.    Laser marking allows fiducials  of controlled dimensions to be added to miniature components , but  presents challenges in tailoring settings to part material and shape. Laser marks are conformal for limited strain environments, and do not involve the addition of organics , moisture, or potential conductive debris   required for most other conformal marking methods. Further minimizing part heating and developing  patterns for low -contrast environments are ongoing topics.    As shown through examples, conformal subsets can be helpful if the trac ked object is non-square by  allowing more speckles to be included in the correlation. There are also a number of features for  conformal subsets that are useful for trajectory tracking. For example, there are ways to enable tracking objects that cross each other's path or become partially obscured by another object. Conformal subsets  can also be evolved through an image sequence to build up the intensity profile if the object is not fully visible at the start of a sequence.  

With the conformal subset advantages, a price is often paid with regards to computation speed. The simplex  solver method in DICe is less efficient, leading to an increase in processing time when compared  to gradient -based methods . To combat this weakness, DICe has made several advances to  improve  parallelization and improving the performance of the simplex method. Methods of in-situ switching  between fast optimization methods and more robust ones are also under development.