Academic Journal
A comparative study of unsupervised machine learning methods for anomaly detection in flight data : case studies from real-world flight operations
| Τίτλος: | A comparative study of unsupervised machine learning methods for anomaly detection in flight data : case studies from real-world flight operations |
|---|---|
| Συγγραφείς: | Jasra, Sameer Kumar, Valentino, Gianluca, Muscat, Alan, Camilleri, Robert |
| Στοιχεία εκδότη: | MDPI AG |
| Έτος έκδοσης: | 2025 |
| Συλλογή: | University of Malta: OAR@UM / L-Università ta' Malta |
| Θεματικοί όροι: | Flight recorders -- Data processing, Machine learning -- Industrial applications, Anomaly detection (Computer security), Aircraft accidents -- Prevention, Aeronautics -- Safety measures |
| Περιγραφή: | This paper provides a comparative study of unsupervised machine learning (ML) methods for anomaly detection in flight data monitoring (FDM). The study applies various unsupervised ML techniques to real-world flight data and compares the results to the current state-of-the-art flight data analysis techniques applied in industry. The results are validated by the industrial experts. The study finds that a hybrid Local Outlier Factor (LOF) approach provides significant advantages compared to the current state of the art and other ML techniques because it requires less hyperparameter tuning, reduces the number of false positives, provides an ability to establish trends amongst the entire fleet and has an ability to investigate anomalies at each timestep within every flight. Finally, the study provides an in-depth review for some of the cases highlighted by the hybrid LOF and discusses the particular cases providing insights from an academic and flight safety/operational point of view. The analysis conducted by the human expert regarding the outcomes produced by an ML technique is predominantly absent in scholarly research, thereby offering extra value. The study presents a compelling argument for transitioning from the current approach, based on analyzing occurrences through the exceedances of a threshold value, towards an ML-based method which provides a proactive nature of data analysis. The study shows that there is an untapped opportunity to process flight data and achieve valuable information for enhancing air transport safety and improved aviation operations. ; peer-reviewed |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | English |
| Relation: | https://www.um.edu.mt/library/oar/handle/123456789/137334 |
| DOI: | 10.3390/aerospace12020151 |
| Διαθεσιμότητα: | https://www.um.edu.mt/library/oar/handle/123456789/137334 https://doi.org/10.3390/aerospace12020151 |
| Rights: | info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder |
| Αριθμός Καταχώρησης: | edsbas.CDD221BA |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://www.um.edu.mt/library/oar/handle/123456789/137334# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: A comparative study of unsupervised machine learning methods for anomaly detection in flight data : case studies from real-world flight operations – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jasra%2C+Sameer+Kumar%22">Jasra, Sameer Kumar</searchLink><br /><searchLink fieldCode="AR" term="%22Valentino%2C+Gianluca%22">Valentino, Gianluca</searchLink><br /><searchLink fieldCode="AR" term="%22Muscat%2C+Alan%22">Muscat, Alan</searchLink><br /><searchLink fieldCode="AR" term="%22Camilleri%2C+Robert%22">Camilleri, Robert</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: MDPI AG – Name: DatePubCY Label: Publication Year Group: Date Data: 2025 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of Malta: OAR@UM / L-Università ta' Malta – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Flight+recorders+--+Data+processing%22">Flight recorders -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning+--+Industrial+applications%22">Machine learning -- Industrial applications</searchLink><br /><searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Aircraft+accidents+--+Prevention%22">Aircraft accidents -- Prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Aeronautics+--+Safety+measures%22">Aeronautics -- Safety measures</searchLink> – Name: Abstract Label: Description Group: Ab Data: This paper provides a comparative study of unsupervised machine learning (ML) methods for anomaly detection in flight data monitoring (FDM). The study applies various unsupervised ML techniques to real-world flight data and compares the results to the current state-of-the-art flight data analysis techniques applied in industry. The results are validated by the industrial experts. The study finds that a hybrid Local Outlier Factor (LOF) approach provides significant advantages compared to the current state of the art and other ML techniques because it requires less hyperparameter tuning, reduces the number of false positives, provides an ability to establish trends amongst the entire fleet and has an ability to investigate anomalies at each timestep within every flight. Finally, the study provides an in-depth review for some of the cases highlighted by the hybrid LOF and discusses the particular cases providing insights from an academic and flight safety/operational point of view. The analysis conducted by the human expert regarding the outcomes produced by an ML technique is predominantly absent in scholarly research, thereby offering extra value. The study presents a compelling argument for transitioning from the current approach, based on analyzing occurrences through the exceedances of a threshold value, towards an ML-based method which provides a proactive nature of data analysis. The study shows that there is an untapped opportunity to process flight data and achieve valuable information for enhancing air transport safety and improved aviation operations. ; peer-reviewed – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://www.um.edu.mt/library/oar/handle/123456789/137334 – Name: DOI Label: DOI Group: ID Data: 10.3390/aerospace12020151 – Name: URL Label: Availability Group: URL Data: https://www.um.edu.mt/library/oar/handle/123456789/137334<br />https://doi.org/10.3390/aerospace12020151 – Name: Copyright Label: Rights Group: Cpyrght Data: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder – Name: AN Label: Accession Number Group: ID Data: edsbas.CDD221BA |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/aerospace12020151 Languages: – Text: English Subjects: – SubjectFull: Flight recorders -- Data processing Type: general – SubjectFull: Machine learning -- Industrial applications Type: general – SubjectFull: Anomaly detection (Computer security) Type: general – SubjectFull: Aircraft accidents -- Prevention Type: general – SubjectFull: Aeronautics -- Safety measures Type: general Titles: – TitleFull: A comparative study of unsupervised machine learning methods for anomaly detection in flight data : case studies from real-world flight operations Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jasra, Sameer Kumar – PersonEntity: Name: NameFull: Valentino, Gianluca – PersonEntity: Name: NameFull: Muscat, Alan – PersonEntity: Name: NameFull: Camilleri, Robert IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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