Development of CNN and LSTM fusion model for early fault warning in hydro turbine units.

Yu Zhu, Yanmu Chen, Xiaxia Xiang, Jiayu Wang, Shengbo Wang, Xiaomo Jiang, Yeming Lu

Abstract read

In one paragraph

Article in iScience, 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

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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

7 authors.

Yu ZhuXi'an Research inst. of Hi-tech, Xi'an, Shaanxi 710025, P.R. China.

Yanmu ChenSchool of Energy and Power Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P.R. China.

Xiaxia XiangSchool of Energy and Power Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P.R. China.

Jiayu WangSchool of Energy and Power Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P.R. China.

Shengbo WangSchool of Energy and Power Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P.R. China.

Xiaomo JiangSchool of Energy and Power Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P.R. China.

Yeming LuSchool of Energy and Power Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early fault detection is important for the safe and stable operation of large hydro turbine units under complex and variable operating conditions. This study develops an early fault warning framework for high dimensional multisource monitoring data by integrating Kendall rank correlation-based parameter grouping, systematic data governance, sparse principal component analysis, and a CNN LSTM prediction model. Fifty two operating parameters are organized into six groups and transformed into six sparse state features. The CNN LSTM model captures local correlations and temporal evolution, achieving R

Indexed as

CNNconvolutional neural networkearly fault warninglarge hydro-turbine unitslong short-term memoryLSTMsparse principal component analysisSPCA

Identifiers

PMID42866724

DOI10.1016/j.isci.2026.117740

PMCPMC13657364

What OpenQuestion holds

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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.