FCRNet: Fast Fourier convolutional residual network for ventilator bearing fault diagnosis.

Yu Cao, Yongzhi Du, Likun Le, Xiaoxue Li, Yanfang Gao

Abstract read

In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

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

The trial behind it

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Yu CaoSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Beijing, China.ORCID https://orcid.org/0009-0001-0994-4134

Yongzhi DuSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Beijing, China.

Likun LeSchool of Mechanical and Electrical Engineering, China University of Mining and Technology, Beijing, China.

Xiaoxue LiSchool of Mechanical and Transportation Engineering, Ordos Institute of Applied Technology, Inner Mongolia, China.

Yanfang GaoSchool of Mechanical and Transportation Engineering, Ordos Institute of Applied Technology, Inner Mongolia, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents FCRNet, a Fast Fourier Convolution Residual Network, tailored for fault diagnosis of mine ventilation bearings under complex operating conditions. By integrating residual learning with Fast Fourier Convolution (FFC), FCRNet employs a dual-branch architecture to effectively capture local spatial features and global frequency patterns. A Spectral Transformation (ST) module achieves unified processing of multi-scale spatial and frequency information by integrating local Fourier features (LFF), global fourier features (GFF), and local time-domain features (LF), overcoming the limitations of conventional convolutional approaches. The testing results on publicly available datasets and our self-built platform validate that the proposed method outperforms several existing fault diagnosis methods at various noise levels, providing strong support for the condition monitoring of mine ventilation.

Indexed as

Neural Networks, ComputerVentilators, MechanicalAlgorithmsFourier AnalysisHumans

Identifiers

PMID40644364

DOI10.1371/journal.pone.0327342

PMCPMC12250675

What OpenQuestion holds

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LicenceCC BY

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.