Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
  Detection and identification of cylinder misfire in small aircraft engine in different operating conditions by linear and non-linear properties of frequency components

Syta, A., Czarnigowski, J., Jakliński, P., Marwan, N. (2023): Detection and identification of cylinder misfire in small aircraft engine in different operating conditions by linear and non-linear properties of frequency components. - Measurement, 223, 113763.
https://doi.org/10.1016/j.measurement.2023.113763

Item is

Dateien

einblenden: Dateien
ausblenden: Dateien
:
syta_1-s2.0-S0263224123013271-main.pdf (Verlagsversion), 2MB
Name:
syta_1-s2.0-S0263224123013271-main.pdf
Beschreibung:
-
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Syta, Arkadiusz1, Autor
Czarnigowski, Jacek1, Autor
Jakliński, Piotr1, Autor
Marwan, Norbert2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: We suggest an approach for detecting and identifying ignition failure on a internal combustion engine used in aviation through the analysis of vibration time series. The research is carried out at the experimental stage, where time series of vibrations are collected from sensors installed in various parts of the facility at various rotational speeds and various operating conditions (no failure/failure of a selected piston). The time series were decomposed into periodic components centered around dominant frequencies. Data with greater dimensionality was statistically described using linear and non-linear indicators in short time windows, and labeled accordingly. Instead of examining the statistical significance of the characteristics of individual groups, machine learning classification methods were used, which allowed to distinguish the operating state of the engine (damaged/undamaged), and also to identify a specific unfired cylinder. The use of non-linear indicators allowed us to obtain 100% classification accuracy with a small number of samples.

Details

einblenden:
ausblenden:
Sprache(n): eng - Englisch
 Datum: 2023-11-082023-12-01
 Publikationsstatus: Final veröffentlicht
 Seiten: 9
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.measurement.2023.113763
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
Model / method: Nonlinear Data Analysis
OATYPE: Hybrid Open Access
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Measurement
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 223 Artikelnummer: 113763 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals338
Publisher: Elsevier