Volumetric Tooth Wear Measurement of Scraper Conveyor Sprocket Using Shape from Focus-Based Method

Abstract

Volumetric tooth wear measurement is important to assess the life of scraper conveyor sprocket. A shape from focus-based method is used to measure scraper conveyor sprocket tooth wear. This method reduces the complexity of the process and improves the accuracy and efficiency of existing methods. A prototype set of sequence images taken by the camera facing the sprocket teeth is collected by controlling the fabricated track movement. In this method, a normal distribution operator image filtering is employed to improve the accuracy of an evaluation function value calculation. In order to detect noisy pixels, a normal operator is used, which involves with using a median filter to retain as much of the original image information as possible. In addition, an adaptive evaluation window selection method is proposed to address the difficulty associated with identifying an appropriate evaluation window to calculate the focused evaluation value. The shape and size of the evaluation window are autonomously determined using the correlation value of the grey scale co-occurrence matrix generated from the measured pixels’ neighbourhood pixels. A reverse engineering technique is used to quantitatively verify the shape volume recovery accuracy of different evaluation windows. The test results demonstrate that the proposed method can effectively measure sprocket teeth wear volume with an accuracy up to 97.23%.