Evidence map›Paper›PMID 41013097›Full record

ArticleSensors (Basel, Switzerland)2025

Strain Sensor-Based Fatigue Prediction for Hydraulic Turbine Governor Servomotor in Complementary Energy Systems.

Hong Hua, Zhizhong Zhang, Xiaobing Liu, Wanquan Deng

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

4 authors.

Hong HuaKey Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu 610039, China.ORCID 0000-0003-1522-6603
Zhizhong ZhangCHN Energy Dadu River Zhentouba Power Generation Co., Ltd., Leshan 614900, China.
Xiaobing LiuKey Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu 610039, China.
Wanquan DengKey Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu 610039, China.

Funding

The National Natural ScienceFoundation of China Regional Innovation and Development Joint Fund project;the Open Research Subject of Key Laboratory of Fluid and Power Machinery (Xihua University), Ministry of Education No. U23A20669; No. szjj2024-LTDL-2024010
6 · The paper itself

Abstract

Hydraulic turbine governor servomotors in wind solar hydro complementary energy systems face significant fatigue failure challenges due to high-frequency regulation. This study develops an intelligent fatigue monitoring and prediction system based on strain sensors, specifically designed for the frequent regulation requirements of complementary systems. A multi-point monitoring network was constructed using resistive strain sensors, integrated with temperature and vibration sensors for multimodal data fusion. Field validation was conducted at an 18.56 MW hydroelectric unit, covering guide vane opening ranges from 13% to 63%, with system response time <1 ms and a signal-to-noise ratio of 65 dB. A simulation model combining sensor measurements with finite element simulation was established through fine-mesh modeling to identify critical fatigue locations. The finite element analysis results show excellent agreement with experimental measurements (error < 8%), validating the simulation model approach. The fork head was identified as the critical component with a stress concentration factor of 3.4, maximum stress of 51.7 MPa, and predicted fatigue life of 1.2 × 10

Indexed as

fatigue predictionfinite element analysishydraulic turbine governorservomotorstrain sensor

Identifiers

PMID41013097
PMCPMC12473477

What OpenQuestion holds

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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.