Evidence map›Paper›PMID 39001025›Full record

ArticleSensors (Basel, Switzerland)2024

Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions.

Serhii Vladov, Lukasz Scislo, Valerii Sokurenko, Oleksandr Muzychuk, Victoria Vysotska, Serhii Osadchy, Anatoliy Sachenko

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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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.

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
Lukasz ScisloFaculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24, 31-155 Craków, Poland.ORCID 0000-0002-7365-8888
Valerii SokurenkoKharkiv National University of Internal Affairs, Ministry of Internal Affairs of Ukraine, 61080 Kharkiv, Ukraine.
Oleksandr MuzychukKharkiv National University of Internal Affairs, Ministry of Internal Affairs of Ukraine, 61080 Kharkiv, Ukraine.
Victoria VysotskaInformation Systems and Networks Department, Lviv Polytechnic National University, 12 Bandera Street, 79013 Lviv, Ukraine.ORCID 0000-0001-6417-3689
Serhii OsadchyFlight Operation and Flight Safety Department, Flight Academy of the National Aviation University, 1 Chobanu Stepana Street, 25005 Kropyvnytskyi, Ukraine.
Anatoliy SachenkoResearch Institute for Intelligent Computer Systems, West Ukrainian National University, 11 Lvivska Street, 46009 Ternopil, Ukraine.ORCID 0000-0002-0907-3682

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The article's main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network has been developed that integrates closed loops for the helicopter turboshaft engine parameters, which are regulated based on the filtering method. This made achieving almost 100% (0.995 or 99.5%) accuracy possible and reduced the loss function to 0.005 (0.5%) after 280 training epochs. An algorithm has been developed for neural network training based on the errors in backpropagation for closed loops, integrating the helicopter turboshaft engine parameters regulated based on the filtering method. It combines increasing the validation set accuracy and controlling overfitting, considering error dynamics, which preserves the model generalization ability. The adaptive training rate improves adaptation to the data changes and training conditions, improving performance. It has been mathematically proven that the helicopter turboshaft engine parameters regulating neural network closed-loop integration using the filtering method, in comparison with traditional filters (median-recursive, recursive and median), significantly improve efficiency. Moreover, that enables reduction of the errors of the 1st and 2nd types: 2.11 times compared to the median-recursive filter, 2.89 times compared to the recursive filter, and 4.18 times compared to the median filter. The achieved results significantly increase the helicopter turboshaft engine sensor readings accuracy (up to 99.5%) and reliability, ensuring aircraft efficient and safe operations thanks to improved filtering methods and neural network data integration. These advances open up new prospects for the aviation industry, improving operational efficiency and overall helicopter flight safety through advanced data processing technologies.

Indexed as

errorfiltrationhelicopter turboshaft engineintegrationneural networksensor

Identifiers

PMID39001025
PMCPMC11244029

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LicenceCC BY
Read underepoch 390

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.