Abstract
With the rapid development of digital information and multimedia, image processing system is being widely used. Based on the basic theory of ferrography wear analysis, combined with computer image processing technology and pattern recognition theory, this paper extracts and analyzes the features of ferrography wear images caused by different oilfield underground mining machines. The evaluation index of foreign object detection accuracy is given to measure the accuracy of the detection algorithm. Through the analysis of connectivity, the eigenvalues of foreign particle targets in the collected images are obtained, and the eigenvalues of the targets extracted from the images basically have little change. By analyzing the wear failure formation mechanism and wear characteristics of typical oilfield underground mining machinery parts, it lays a solid theoretical foundation for the identification of abrasive morphology characteristics, the judgment of wear types and positions, and the determination of fault types.
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