Shibo Fan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Shibo Fan has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Jie You
Ocean College, China University of Geosciences Beijing, Beijing 100083, China
1 shared publication - Lianfu Wang
College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
1 shared publication - Qinghai Yu
Alumni and Social Cooperation Office, China University of Geosciences Beijing, Beijing 100083, China
1 shared publication - Zheying Zong
College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
1 shared publication - Zhou Zhang
College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
1 shared publication
Latest Publications
- Open Access
Real-Time Ferrogram Segmentation of Wear Debris Using Multi-Level Feature Reused Unet
The real-time monitoring and fault diagnosis of modern machinery and equipment impose higher demands on equipment maintenance, with the extraction of morphological characteristics of wear debris in lubricating oil emerging as a critical approach for real-time monitoring of wear, holding significant importance in the field. The online visual ferrograph (OLVF) technique serves as a representative method in this study. Various semantic segmentation approaches, such as DeepLabV3+, PSPNet, Segformer, Unet, and other models, are employed to process the oil wear particle image for conducting comparative experiments. In order to accurately segment the minute wear debris in oil abrasive images and mitigate the influence of reflection and bubbles, we propose a multi-level feature reused Unet (MFR Unet) that enhances the residual link strategy of Unet for improved identification of tiny wear debris in ferrograms, leading to superior segmentation results.
Open AccessReal-Time Ferrogram Segmentation of Wear Debris Using Multi-Level Feature Reused Unet
The real-time monitoring and fault diagnosis of modern machinery and equipment impose higher demands on equipment maintenance, with the extraction of morphological characteristics of wear debris in lubricating oil emerging as a critical approach for real-time monitoring of wear, holding significant importance in the field. The online visual ferrograph (OLVF) technique serves as a representative method in this study. Various semantic segmentation approaches, such as DeepLabV3+, PSPNet, Segformer, Unet, and other models, are employed to process the oil wear particle image for conducting comparative experiments. In order to accurately segment the minute wear debris in oil abrasive images and mitigate the influence of reflection and bubbles, we propose a multi-level feature reused Unet (MFR Unet) that enhances the residual link strategy of Unet for improved identification of tiny wear debris in ferrograms, leading to superior segmentation results.