Statistical Image Processing of Vane Wear

Objective: Investigate usage of statistical image processing to quantify turbo vane wear

Technology Used: I’m attempting this using purely open-source, free technology available on the internet:

  1. ImageMagick – For command line based auto processing of images []
  2. R – For statistical computing of image data []
  3. Arduino – Open source microcontroller for camera control, indexing table etc []

Test Method:

  • Nozzle vane module gets placed on some sort of an indexing table
  • The camera mount is designed such that the camera points perpendicular to the leading edge of a vane
  • The indexing will allow the table to rotate, so that we can capture pictures of all 14 vanes
  • The camera captures pictures of the leading edge of each vane, stores it on the computer
  • R scripts run ImageMagick internally to isolate the edges of interest and perform statistical calculations

Work Done So Far:

The Setup:

  • Created a platform that can be indexed to each vane; It’s leveled and mounted on an tripod head.
  • Using a 7D with a 105mm Nikkor Macro lens, set to Manual image adjustments & Manual Focus
  • Lighting – Tried a couple of techniques. The analysis and result images shown below is for incident light from a window. The code and the setup pictures show a backlight technique using a red card to filter red light only. This creates a silhouette image of the vane with the ‘non-vane’ portion in red… makes it easy to detect the edge! Extract the Red portion of the RYG information and apply the Canny edge detection algorithm.

Original image of a turbo vane, returned from the field, the Canny Edge detection algorithm, and final extracted edge:

Extracted Data:
Y Axis – Distance from base of vane to the top edge
X Axis expanded to show deviations from the nominal vane edge (which will be a straight line).


Working code on Github :

There’s a lot more to write about the workings of the code and what I’ve learnt. But, this is all I’ll write for now.


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This entry was posted on May 23, 2015 by in Analytics and tagged , , .


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