Real-Time Ferrogram Segmentation of Wear Debris Using Multi-Level Feature Reused Unet.

Jie You, Shibo Fan, Qinghai Yu, Lianfu Wang, Zhou Zhang, Zheying Zong

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jie YouOcean College, China University of Geosciences Beijing, Beijing 100083, China.

Shibo FanSchool of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China.

Qinghai YuAlumni and Social Cooperation Office, China University of Geosciences Beijing, Beijing 100083, China.

Lianfu WangCollege of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.

Zhou ZhangCollege of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.

Zheying ZongCollege of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.

Funding

the Inner Mongolia Autonomous Region Military-civilian Integration Key Scientific Research Projects and Soft Scientific Research Projects (JMZD202303). JMZD202303

6 · The paper itself

Abstract

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.

Indexed as

convolutional neural networkdebris imagesemantic segmentation

Identifiers

PMID38676061

DOI10.3390/s24082444

PMCPMC11054310

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.