Data-Driven Predictive Analytics for Dynamic Aviation Systems: Optimising Fleet Maintenance and Flight Operations Through Machine Learning

Elmin Marevac, Esad Kadušić, Natasa Živić (), Dženan Hamzić and Narcisa Hadžajlić
Additional contact information
Elmin Marevac: Polytechnic Faculty, University of Zenica, 72000 Zenica, Bosnia and Herzegovina
Esad Kadušić: Faculty of Educational Sciences, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
Natasa Živić: Faculty of Digital Transformation, Leipzig University of Applied Sciences, 04277 Leipzig, Germany
Dženan Hamzić: Polytechnic Faculty, University of Zenica, 72000 Zenica, Bosnia and Herzegovina
Narcisa Hadžajlić: Polytechnic Faculty, University of Zenica, 72000 Zenica, Bosnia and Herzegovina

Future Internet, 2025, vol. 17, issue 11, 1-33

Abstract: The aviation industry operates as a complex, dynamic system generating vast volumes of data from aircraft sensors, flight schedules, and external sources. Managing this data is critical for mitigating disruptive and costly events such as mechanical failures and flight delays. This paper presents a comprehensive application of predictive analytics and machine learning to enhance aviation safety and operational efficiency. We address two core challenges: predictive maintenance of aircraft engines and forecasting flight delays. For maintenance, we utilise NASA’s C-MAPSS simulation dataset to develop and compare models, including one-dimensional convolutional neural networks (1D CNNs) and long short-term memory networks (LSTMs), for classifying engine health status and predicting the Remaining Useful Life (RUL), achieving classification accuracy up to 97%. For operational efficiency, we analyse historical flight data to build regression models for predicting departure delays, identifying key contributing factors such as airline, origin airport, and scheduled time. Our methodology highlights the critical role of Exploratory Data Analysis (EDA), feature selection, and data preprocessing in managing high-volume, heterogeneous data sources. The results demonstrate the significant potential of integrating these predictive models into aviation Business Intelligence (BI) systems to transition from reactive to proactive decision-making. The study concludes by discussing the integration challenges within existing data architectures and the future potential of these approaches for optimising complex, networked transportation systems.

Keywords: machine learning; aviation safety; remaining useful life (RUL); predictive maintenance; flight delay prediction; big data; business intelligence (BI); C-MAPSS; dynamic systems (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/17/11/508/pdf (application/pdf)
https://www.mdpi.com/1999-5903/17/11/508/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:17:y:2025:i:11:p:508-:d:1787076

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().