Evidence map›Paper›PMID 42225786›Full record

ArticleScientific reports2026

Life prediction model of feedback rod in electro-hydraulic servo valve.

Xiaonan Pan, Jian Kang, Jianrui Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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

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

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

3 authors.

Xiaonan PanSchool of Intelligent Manufacturing, Longdong University, Qingyang, 745000, China. panxiaonan007@163.com.
Jian KangSchool of Automation, Northwestern Polytechnical University, Xian, 710072, China.
Jianrui ZhangSchool of Intelligent Manufacturing, Longdong University, Qingyang, 745000, China.

Funding

Data Availability:All data generated or analysed during this study are included in this published article. 2025B-215Key Research and Development Program of Gansu Province-Industrial Project 25YFGM002Longdong University Horizontal Research Project HXZK2547Qingyang City Major Science and Technology Project-Industrial Field Project 2025JY1005
6 · The paper itself

Abstract

Metallic elastic elements serve as critical structural components in advanced electro mechanical assemblies, where cyclic loading induces progressive rigidity reduction. Within electro-hydraulic servo valves (EHSV), the feedback mechanism constitutes an essential closed-loop control element whose mechanical property deterioration directly compromises system precision and operational stability. A predictive methodology is introduced for estimating component lifespan under dynamic loading conditions through integrated System Performance Modeling (SPM) and Stiffness Degradation Prediction (SDP) frameworks. Given physical constraints precluding direct rigidity assessment, this approach leverages externally observable parameters for indirect condition monitoring while SPM supplies necessary loading profiles to SDP. The SDP framework integrates continuum damage mechanics principles with machine learning algorithms, with its output continuously refining SPM structural parameters. Through synchronized simulation protocols, this coupled methodology quantifies component rigidity evolution and evaluates system performance metrics to determine remaining operational lifespan. This integrated approach establishes a foundation for predictive maintenance in precision hydraulic control systems.

Indexed as

Continuum damage mechanicsElectro hydraulic servo valveMachine learning algorithmsStiffness evolutionStructural health monitoring

Identifiers

PMID42225786
PMCPMC13338148

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.