Research on defect detection method of bearing dust cover based on machine vision and multi-feature fusion algorithm

Abstract

During the assembly process of deep groove ball bearings, due to defective parts and unqualified assembly process, various indentations and scratches on the dust cover will often result in reducing the service life and reliability of the bearing. Therefore, the online monitoring of the assembly quality of the dust cover ensures the necessary detection process of the bearing surface quality. This paper proposed a bearing dust cover defect detection method based on machine vision and multi-feature fusion algorithm, which can effectively detect bearings with dust cover defects. The algorithm first performs Laplace transform and Sobel operator image enhancement on the collected bearing images. Extract and fuse multi-source fault feature with SIFT-BoVW and GLCM-Hu methods. Machine learning and deep learning models were constructed, and the performance of each model was compared through feature visualization and misclassified analysis. The results show that the extracted multi-source features are more representative and robust. The SIFT-BoVW-GS-SVM model achieved the best detection results in detecting bearing dust cover defects with an accuracy of 91.11%. The processing and program detection time for each bearing image is about 0.019s. The accuracy and speed of detection and judgment meet the needs of online defect detection of bearing dust cover.

AI-enabled industrial equipment monitoring, diagnosis and health management

“…Hao et al [14] introduced a machine vision-based approach for detecting defects in bearing dust covers during the assembly process, with the goal of enhancing the longevity and reliability of bearings. Huang et al [15] presented a feature fusion-based method for measuring grinding surface roughness, aiming to enhance feature extraction and generalization capabilities in deep learning models for this specific task.…”