Evidence map›Paper›PMID 38894342›Full record

ArticleSensors (Basel, Switzerland)2024

Swing Trend Prediction of Main Guide Bearing in Hydropower Units Based on MFS-DCGNN.

Xu Li, Zhuofei Xu, Pengcheng Guo

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Xu LiSchool of Water Resources and Hydroelectric Engineering, Xi'an University of Technology, Xi'an 710048, China.
Zhuofei XuSchool of Water Resources and Hydroelectric Engineering, Xi'an University of Technology, Xi'an 710048, China.ORCID 0000-0003-1477-7067
Pengcheng GuoSchool of Water Resources and Hydroelectric Engineering, Xi'an University of Technology, Xi'an 710048, China.ORCID 0000-0002-1249-2300

Funding

National Natural Science Foundation of China 51839010Scientific Research Program for Youth Innovation Team Construction of the Shaanxi Provincial Department of Education under 22JP057The Youth Innovation Team of Shaanxi Universities 2020-29
6 · The paper itself

Abstract

Hydropower units are the core equipment of hydropower stations, and research on the fault prediction and health management of these units can help improve their safety, stability, and the level of reliable operation and can effectively reduce costs. Therefore, it is necessary to predict the swing trend of these units. Firstly, this study considers the influence of various factors, such as electrical, mechanical, and hydraulic swing factors, on the swing signal of the main guide bearing

Indexed as

feature selectionhydropower unitsMFS-DCGNN algorithmswing trend prediction

Identifiers

PMID38894342
PMCPMC11175333

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.