Evidence map›Paper›PMID 39409532›Full record

ArticleSensors (Basel, Switzerland)2024

Intelligent Method of Identifying the Nonlinear Dynamic Model for Helicopter Turboshaft Engines.

Serhii Vladov, Arkadiusz Banasik, Anatoliy Sachenko, Wojciech M Kempa, Valerii Sokurenko, Oleksandr Muzychuk, Piotr Pikiewicz, Agnieszka Molga, Victoria Vysotska

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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

9 authors.

Serhii VladovDepartment of Scientific Work Organization and Gender Issues, Kremenchuk Flight College of Kharkiv National University of Internal Affairs, 17/6, Peremohy Street, 39605 Kremenchuk, Ukraine.ORCID 0000-0001-8009-5254
Arkadiusz BanasikDepartment of Mathematical Methods in Technics and Informatics, Silesian University of Technology, 44-100 Gliwice, Poland.ORCID 0000-0002-4267-2783
Anatoliy SachenkoResearch Institute for Intelligent Computer Systems, West Ukrainian National University, 11 Lvivska Street, 46009 Ternopil, Ukraine.ORCID 0000-0002-0907-3682
Wojciech M KempaDepartment of Mathematical Methods in Technics and Informatics, Silesian University of Technology, 44-100 Gliwice, Poland.ORCID 0000-0001-9476-2070
Valerii SokurenkoDepartment of Information Systems and Technologies, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine.
Oleksandr MuzychukDepartment of Information Systems and Technologies, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine.
Piotr PikiewiczDepartment of Mathematical Methods in Technics and Informatics, Silesian University of Technology, 44-100 Gliwice, Poland.ORCID 0000-0002-1348-9381
Agnieszka MolgaDepartment of Teleinformatics, Kazimierz Pulaski University of Radom, 26-600 Radom, Poland.
Victoria VysotskaInformation Systems and Networks Department, Lviv Polytechnic National University, 12, Bandera Street, 79013 Lviv, Ukraine.ORCID 0000-0001-6417-3689

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research focused on the helicopter turboshaft engine dynamic model, identifying task solving in unsteady and transient modes (engine starting and acceleration) based on sensor data. It is known that about 85% of helicopter turboshaft engines operate in steady-state modes, while only around 15% operate in unsteady and transient modes. Therefore, developing dynamic multi-mode models that account for engine behavior during these modes is a critical scientific and practical task. The dynamic model for starting and acceleration modes has been further developed using on-board parameters recorded by sensors (gas-generator rotor r.p.m., free turbine rotor speed, gas temperature in front of the compressor turbine, fuel consumption) to achieve a 99.88% accuracy in identifying the dynamics of these parameters. An improved Elman recurrent neural network with dynamic stack memory was introduced, enhancing the robustness and increasing the performance by 2.7 times compared to traditional Elman networks. A theorem was proposed and proven, demonstrating that the total execution time for

Indexed as

accuracydynamic modelElman recurrent neural network with dynamic stack memoryengine starting and accelerationhelicopter turboshaft enginesidentifyingsensorstraining

Identifiers

PMID39409532
PMCPMC11479260

What OpenQuestion holds

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LicenceCC BY
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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.