Evidence map›Paper›PMID 40594798›Full record

ArticleScientific reports2025

Bearing fault diagnosis based on cross image multi-attention mechanism.

Yupeng Liu, Weinan Zheng, Ying Du, Yuehui Wang, Jian Jin, Miao Yu

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

The trial behind it

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

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

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.

Yupeng LiuSchool of Electrical and Mechanical Engineering, Jilin University of Architecture and Technology, Changchun, 130114, China. 1589356167@qq.com.
Weinan ZhengSchool of Electrical and Mechanical Engineering, Jilin University of Architecture and Technology, Changchun, 130114, China.
Ying DuSchool of Electrical and Mechanical Engineering, Jilin University of Architecture and Technology, Changchun, 130114, China.
Yuehui WangSchool of Electrical and Mechanical Engineering, Jilin University of Architecture and Technology, Changchun, 130114, China.
Jian JinSchool of Electrical and Mechanical Engineering, Jilin University of Architecture and Technology, Changchun, 130114, China.
Miao YuSchool of Electrical and Mechanical Engineering, Jilin University of Architecture and Technology, Changchun, 130114, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bearings are crucial components of rotating machinery, and fault diagnosis is essential for ensuring the safe operation of mechanical systems. Neural networks, commonly used in bearing fault diagnosis, are effective in extracting deep features from fault signals but often fail to emphasize critical information. We propose a fault diagnosis method that integrates a cross-image multi-attention mechanism with a residual neural network. The collected vibration signals are first preprocessed using VMD-GAF and then fed into the network for fault detection. The results demonstrate that the CIMAM-ResNet18 model significantly enhances the robustness of signal processing, achieving an accuracy of 98.00% when tested on the experimental platform.

Indexed as

Bearing fault diagnosisCIMAM-ResNet18Multi-attention mechanism

Identifiers

PMID40594798
PMCPMC12218351

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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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.