期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 70, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2021.3099573
关键词
Automatically labeled; convolutional neural network (CNN); deep learning; ferrography; wear debris segmentation
资金
- National Natural Science Foundation of China [51705057, 51675408]
- Chongqing Natural Science Foundation [cstc2020jcyjmsxmX0895]
This paper proposes a lightweight residual U-net convolutional neural network model for extracting lubricating oil wear debris morphological features, and experimental results demonstrate the accuracy and anti-interference performance of this method.
Extraction of lube oil wear debris morphological features is an important means for real-time monitoring of equipment wear, and online visual ferrograph (OLVF) is one of the representative technologies. In the current OLVF wear monitoring, the transmission ferrogram (TF) is basically relied on, but the more informative reflection ferrogram (RF) has not yet been applied, because its complex surface color distribution and bubble interference make it difficult to segment the RE Accordingly, a convolutional neural network (CNN) model called lightweight residual U-net (Res-UNet) is constructed in this article. Simultaneously, with both RFs and TFs, an automatic labeling method is proposed to label the RFs and make a training dataset to implement network training. The experimental results demonstrate that the trained network can achieve accurate segmentation of RFs with excellent anti-interference performance. The proposed method lays the foundation for the feature extraction of reflection OLVF ferrograms and provides an alternative image segmentation method for other image wear debris sensors.
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