Advanced Spectral Diagnostics of Jet Engine Vibrations Using Non-Contact Laser Vibrometry and Fourier Methods

Prokopowicz, W; Ciupek, B; Maciag, A; Gajewski, T; Sielicki, PW

Advanced Spectral Diagnostics of Jet Engine Vibrations Using Non-Contact Laser Vibrometry and Fourier Methods

Prokopowicz, W Ciupek, B Maciag, A Gajewski, T Sielicki, PW

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: ENERGIES, 2025, 18, (18)
Issue Date:: 2025-09-11

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Field	Value	Language
dc.contributor.author	Prokopowicz, W
dc.contributor.author	Ciupek, B
dc.contributor.author	Maciag, A
dc.contributor.author	Gajewski, T
dc.contributor.author	Sielicki, PW
dc.date.accessioned	2025-11-21T06:27:44Z
dc.date.available	2025-11-21T06:27:44Z
dc.date.issued	2025-09-11
dc.identifier.citation	ENERGIES, 2025, 18, (18)
dc.identifier.issn	1996-1073
dc.identifier.issn	1996-1073
dc.identifier.uri	http://hdl.handle.net/10453/190757
dc.description.abstract	This study presents an advanced diagnostic methodology for assessing mechanical faults in high-performance jet engines using non-contact laser vibrometry and Fourier-based vi-bration analysis. Focusing on Pratt & Whitney F100-PW-229 engines used in F-16 aircraft, thise research identifies critical measurement locations, including the gearbox, turbine, and compressor supports. High-resolution vibration signals were collected under test bench conditions and processed using fFast Fourier tTransform (FFT) techniques to extract frequency-domain features indicative of rotor imbalances, bearing wear, and structural anomalies. Comparative analysis between nominal and degraded engines confirmed strong correlations between analytical predictions and empirical spectral patterns. Thise study introduces a signal processing framework combining time–frequency analysis with Relief-F-based feature selection, laying the groundwork for future integration with ma-chine learning algorithms. This non-intrusive, efficient diagnostic method supports early fault detection, enhances engine availability, and contributes to the development of a na-tional vibration reference database, especially vital in the absence of OEM-supplied tools.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	ENERGIES
dc.relation.isbasedon	10.3390/en18184837
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	02 Physical Sciences, 09 Engineering
dc.subject.classification	33 Built environment and design
dc.subject.classification	40 Engineering
dc.subject.classification	51 Physical sciences
dc.title	Advanced Spectral Diagnostics of Jet Engine Vibrations Using Non-Contact Laser Vibrometry and Fourier Methods
dc.type	Journal Article
utslib.citation.volume	18
utslib.for	02 Physical Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-11-21T06:27:42Z
pubs.issue	18
pubs.publication-status	Published
pubs.volume	18
utslib.citation.issue	18

Abstract:

This study presents an advanced diagnostic methodology for assessing mechanical faults in high-performance jet engines using non-contact laser vibrometry and Fourier-based vi-bration analysis. Focusing on Pratt & Whitney F100-PW-229 engines used in F-16 aircraft, thise research identifies critical measurement locations, including the gearbox, turbine, and compressor supports. High-resolution vibration signals were collected under test bench conditions and processed using fFast Fourier tTransform (FFT) techniques to extract frequency-domain features indicative of rotor imbalances, bearing wear, and structural anomalies. Comparative analysis between nominal and degraded engines confirmed strong correlations between analytical predictions and empirical spectral patterns. Thise study introduces a signal processing framework combining time–frequency analysis with Relief-F-based feature selection, laying the groundwork for future integration with ma-chine learning algorithms. This non-intrusive, efficient diagnostic method supports early fault detection, enhances engine availability, and contributes to the development of a na-tional vibration reference database, especially vital in the absence of OEM-supplied tools.

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http://hdl.handle.net/10453/190757