Intelligent Recognition Method for Ferrography Wear Debris Images Using Improved Mask R-CNN Methods
The accurate characterization of wear debris is crucial for assessing the health of rotating engine components and for conducting simulation experiments in debris detection. This study proposed an intelligent recognition method for ferrography wear debris images, leveraging several improved Mask Reg...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Lubricants |
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| Online Access: | https://www.mdpi.com/2075-4442/13/5/208 |
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| author | Xiangwen Xiao Weixuan Zhang Qing Wang Yuan Liu Yishou Wang |
| author_facet | Xiangwen Xiao Weixuan Zhang Qing Wang Yuan Liu Yishou Wang |
| author_sort | Xiangwen Xiao |
| collection | DOAJ |
| description | The accurate characterization of wear debris is crucial for assessing the health of rotating engine components and for conducting simulation experiments in debris detection. This study proposed an intelligent recognition method for ferrography wear debris images, leveraging several improved Mask Region-based Convolutional Neural Network (Mask R-CNN) algorithms to quantitatively calculate both the number of debris particles and their coverage areas. The improvement on the Mask R-CNN focuses on two key aspects: enhancing feature extraction through the feature pyramid network structure and integrating attention mechanisms. The most suitable attention mechanism for wear debris detection was determined through ablation experiments. The improved Mask R-CNN combined with the Convolutional Block Attention Module achieves the best Mean Pixel Accuracy of 87.63% at a processing speed of 7.6 frames per second, demonstrating its high accuracy and efficiency in wear particle segmentation. Furthermore, the quantitative and qualitative analysis of wear debris, including the number and area of debris particles and their classification, provides valuable insights into the severity of wear. These insights are essential for understanding the extent of wear damage and guiding maintenance decisions. |
| format | Article |
| id | doaj-art-e12dbac556954261bfa8d15e4b605229 |
| institution | Kabale University |
| issn | 2075-4442 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Lubricants |
| spelling | doaj-art-e12dbac556954261bfa8d15e4b6052292025-08-20T03:47:57ZengMDPI AGLubricants2075-44422025-05-0113520810.3390/lubricants13050208Intelligent Recognition Method for Ferrography Wear Debris Images Using Improved Mask R-CNN MethodsXiangwen Xiao0Weixuan Zhang1Qing Wang2Yuan Liu3Yishou Wang4School of Aerospace Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Aerospace Engineering, Xiamen University, Xiamen 361005, ChinaXiamen Airlines, Xiamen 361003, ChinaAECC Hunan Aviation Powerplant Research Institute, Zhuzhou 412000, ChinaSchool of Aerospace Engineering, Xiamen University, Xiamen 361005, ChinaThe accurate characterization of wear debris is crucial for assessing the health of rotating engine components and for conducting simulation experiments in debris detection. This study proposed an intelligent recognition method for ferrography wear debris images, leveraging several improved Mask Region-based Convolutional Neural Network (Mask R-CNN) algorithms to quantitatively calculate both the number of debris particles and their coverage areas. The improvement on the Mask R-CNN focuses on two key aspects: enhancing feature extraction through the feature pyramid network structure and integrating attention mechanisms. The most suitable attention mechanism for wear debris detection was determined through ablation experiments. The improved Mask R-CNN combined with the Convolutional Block Attention Module achieves the best Mean Pixel Accuracy of 87.63% at a processing speed of 7.6 frames per second, demonstrating its high accuracy and efficiency in wear particle segmentation. Furthermore, the quantitative and qualitative analysis of wear debris, including the number and area of debris particles and their classification, provides valuable insights into the severity of wear. These insights are essential for understanding the extent of wear damage and guiding maintenance decisions.https://www.mdpi.com/2075-4442/13/5/208instance segmentationcomputer visionwear debris analysisferrographyconvolutional neural networkmulti-feature fusion |
| spellingShingle | Xiangwen Xiao Weixuan Zhang Qing Wang Yuan Liu Yishou Wang Intelligent Recognition Method for Ferrography Wear Debris Images Using Improved Mask R-CNN Methods Lubricants instance segmentation computer vision wear debris analysis ferrography convolutional neural network multi-feature fusion |
| title | Intelligent Recognition Method for Ferrography Wear Debris Images Using Improved Mask R-CNN Methods |
| title_full | Intelligent Recognition Method for Ferrography Wear Debris Images Using Improved Mask R-CNN Methods |
| title_fullStr | Intelligent Recognition Method for Ferrography Wear Debris Images Using Improved Mask R-CNN Methods |
| title_full_unstemmed | Intelligent Recognition Method for Ferrography Wear Debris Images Using Improved Mask R-CNN Methods |
| title_short | Intelligent Recognition Method for Ferrography Wear Debris Images Using Improved Mask R-CNN Methods |
| title_sort | intelligent recognition method for ferrography wear debris images using improved mask r cnn methods |
| topic | instance segmentation computer vision wear debris analysis ferrography convolutional neural network multi-feature fusion |
| url | https://www.mdpi.com/2075-4442/13/5/208 |
| work_keys_str_mv | AT xiangwenxiao intelligentrecognitionmethodforferrographyweardebrisimagesusingimprovedmaskrcnnmethods AT weixuanzhang intelligentrecognitionmethodforferrographyweardebrisimagesusingimprovedmaskrcnnmethods AT qingwang intelligentrecognitionmethodforferrographyweardebrisimagesusingimprovedmaskrcnnmethods AT yuanliu intelligentrecognitionmethodforferrographyweardebrisimagesusingimprovedmaskrcnnmethods AT yishouwang intelligentrecognitionmethodforferrographyweardebrisimagesusingimprovedmaskrcnnmethods |