Method for Detecting Micron Cracks on a Magnetic Rotor Surface Based on a Support Vector Machine

Abstract

Micro-scale cracks on the surface of a magnetic rotor are difficult to detect because of the formation of stains. A method that is based on computer vision and a support vector machine (SVM) that can accurately and rapidly locate micro-scale cracks are proposed. To further highlight the cracks and reduce interference, the image is pre-processed using improved adaptive median filtering, image sharpening, extract feature point, and an expansion based on mathematical morphology. Next, the feature curves of cracks and stains are extracted as training samples using the feature curve extract algorithm and the region orientation algorithm (ROA) for continuous and discontinuous crack curves images, respectively. SVM is later used to distinguish between the two types of image sources. An improved region orientation algorithm (IROA) is proposed to increase the efficiency of discontinuous crack curves images through online detection. Experiment results show that the accuracy of crack detection using ROA is 97.9%, and the accuracy is 94.7% using IROA. However, the IROA algorithm execution time is 3% that of ROA, and IROA’s average crack detection time is shorter than that of ROA by 51%. The proposed method provides the basis for the automatic online detection of micro-scale cracks on a magnetic rotor surface and paves the way for the identification of cracks on complex morphologies, such as split cracks and circular cracks.