From raw data to monotonic and trendable features reflecting degradation trends in turbofan engines

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dc.contributor.authorFuad, Mohd Fazril Irfan Ahmad
dc.contributor.authorKhan, Samir
dc.contributor.authorErkoyuncu, John Ahmet
dc.date.accessioned2025-06-11T13:57:46Z
dc.date.available2025-06-11T13:57:46Z
dc.date.freetoread2025-06-11
dc.date.issued2024-12-08
dc.date.pubOnline2025-03-25
dc.description.abstractThe performance of prognostic models relies heavily on the form and trend of the extracted features. However, the raw data collected from physical systems are inherently noisy, large in volume, and exhibit significant variability, which makes them unsuitable for direct use in prognostics. These characteristics poorly reflect the degradation behavior of physical systems and contribute to the uncertainty of prognostic outcome. Hence, transforming this data into relevant features and carefully selecting them is crucial for meeting the specific needs of prognostic models. This paper aims to address data processing challenges by focusing on extraction and selection of high-quality monotonic features which clearly reflect the degradation and can reduce prognostics uncertainty. The proposed framework comprises three main stages: Data pre-processing, feature extraction, and feature selection. It includes a fitness analysis to evaluate the monotonicity and trendability of features supplemented by visual inspections to identify relevant features. Applied to the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset from the NASA Ames Prognostics Data Repository, the framework reduces noise, improves feature monotonicity and trendability, and facilitates the selection of useful features - essential aspects for effective prognostic methods.
dc.description.conferencename2024 IEEE 3rd Industrial Electronics Society Annual On-Line Conference (ONCON)
dc.description.sponsorshipThis research was supported by the Centre for Digital Engineering and Manufacturing, Cranfield University, United Kingdom, 10.13039/501100000859
dc.identifier.citationFuad MFIA, Khan S, Erkoyuncu JA. (2024) From raw data to monotonic and trendable features reflecting degradation trends in turbofan engines. In: 2024 IEEE 3rd Industrial Electronics Society Annual On-Line Conference (ONCON). 8-10 December 2024, Beijing, Chinaen_UK
dc.identifier.eisbn979-8-3315-4031-9
dc.identifier.elementsID567479
dc.identifier.isbn979-8-3315-4032-6
dc.identifier.urihttps://doi.org/10.1109/oncon62778.2024.10931435
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/24021
dc.language.isoen
dc.publisherIEEEen_UK
dc.publisher.urihttps://ieeexplore.ieee.org/document/10931435
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4605 Data Management and Data Scienceen_UK
dc.subject46 Information and Computing Sciencesen_UK
dc.subject40 Engineeringen_UK
dc.subjectfeature extractionen_UK
dc.subjectfeature selectionen_UK
dc.subjectmonotonicityen_UK
dc.subjecttrendabilityen_UK
dc.subjectprognosticsen_UK
dc.subjectturbofanen_UK
dc.titleFrom raw data to monotonic and trendable features reflecting degradation trends in turbofan enginesen_UK
dc.typeConference paper
dcterms.coverageBeijing, China
dcterms.dateAccepted2024-11-11
dcterms.temporal.endDate10 Dec 2024
dcterms.temporal.startDate8 Dec 2024

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