Unsupervised anomaly detection method for aero engine gas path based on Memory-AAE.
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| Title: | Unsupervised anomaly detection method for aero engine gas path based on Memory-AAE. |
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| Authors: | Liu, Xingchen1 a1323061826@163.com, Sun, Chenfeng1, Li, Tao1, Ji, Kaifu1, Zhang, Tianqi1 |
| Source: | Insight: Non-Destructive Testing & Condition Monitoring. Apr2026, Vol. 68 Issue 4, p251-259. 9p. |
| Subjects: | Autoencoders, K-means clustering, Robust statistics, Outlier detection, Feature extraction |
| Abstract: | Aero engine gas path anomaly detection serves as a critical safeguard for the normal operation of aircraft. The aero engine gas path data comprises a substantial volume of normal data and a limited number of abnormal data and exhibits the characteristics of high dimensionality and strong coupling. Existing methods have difficulty in effectively extracting deep-level feature information from the original data, which leads to insufficient differentiation between normal and abnormal data, thereby seriously reducing the accuracy of anomaly detection. To address this issue, an unsupervised anomaly detection method based on the memory-adversarial autoencoder (Memory-AAE) is proposed. Firstly, the Memory-AAE model is constructed by integrating the memory network and adversarial training mechanism, which effectively enhances the capability to extract deep-level feature information. Then, a normal sample screening strategy is designed, which clusters the reconstruction errors of the original data by the K-means algorithm to obtain a refined normal sample dataset. Finally, the anomaly scoring mechanism based on Euclidean distance and mean absolute error is adopted to quantify the anomaly degree from both global and local dimensions, which effectively improves the detection capability of complex anomalies. In this paper, gas path anomaly detection experiments indicate that the precision, recall and F1 score of the proposed method reach 0.923, 0.915 and 0.919, respectively, and exhibit excellent robustness to noise, significantly outperforming other methods.. [ABSTRACT FROM AUTHOR] |
| Copyright of Insight: Non-Destructive Testing & Condition Monitoring is the property of British Institute of Non-Destructive Testing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193079579 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Unsupervised anomaly detection method for aero engine gas path based on Memory-AAE. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Xingchen%22">Liu, Xingchen</searchLink><relatesTo>1</relatesTo><i> a1323061826@163.com</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Chenfeng%22">Sun, Chenfeng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Li%2C+Tao%22">Li, Tao</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ji%2C+Kaifu%22">Ji, Kaifu</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Tianqi%22">Zhang, Tianqi</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Insight%3A+Non-Destructive+Testing+%26+Condition+Monitoring%22">Insight: Non-Destructive Testing & Condition Monitoring</searchLink>. Apr2026, Vol. 68 Issue 4, p251-259. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Autoencoders%22">Autoencoders</searchLink><br /><searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+statistics%22">Robust statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Aero engine gas path anomaly detection serves as a critical safeguard for the normal operation of aircraft. The aero engine gas path data comprises a substantial volume of normal data and a limited number of abnormal data and exhibits the characteristics of high dimensionality and strong coupling. Existing methods have difficulty in effectively extracting deep-level feature information from the original data, which leads to insufficient differentiation between normal and abnormal data, thereby seriously reducing the accuracy of anomaly detection. To address this issue, an unsupervised anomaly detection method based on the memory-adversarial autoencoder (Memory-AAE) is proposed. Firstly, the Memory-AAE model is constructed by integrating the memory network and adversarial training mechanism, which effectively enhances the capability to extract deep-level feature information. Then, a normal sample screening strategy is designed, which clusters the reconstruction errors of the original data by the K-means algorithm to obtain a refined normal sample dataset. Finally, the anomaly scoring mechanism based on Euclidean distance and mean absolute error is adopted to quantify the anomaly degree from both global and local dimensions, which effectively improves the detection capability of complex anomalies. In this paper, gas path anomaly detection experiments indicate that the precision, recall and F1 score of the proposed method reach 0.923, 0.915 and 0.919, respectively, and exhibit excellent robustness to noise, significantly outperforming other methods.. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Insight: Non-Destructive Testing & Condition Monitoring is the property of British Institute of Non-Destructive Testing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1784/insi.2026.68.4.251 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 251 Subjects: – SubjectFull: Autoencoders Type: general – SubjectFull: K-means clustering Type: general – SubjectFull: Robust statistics Type: general – SubjectFull: Outlier detection Type: general – SubjectFull: Feature extraction Type: general Titles: – TitleFull: Unsupervised anomaly detection method for aero engine gas path based on Memory-AAE. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Xingchen – PersonEntity: Name: NameFull: Sun, Chenfeng – PersonEntity: Name: NameFull: Li, Tao – PersonEntity: Name: NameFull: Ji, Kaifu – PersonEntity: Name: NameFull: Zhang, Tianqi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13542575 Numbering: – Type: volume Value: 68 – Type: issue Value: 4 Titles: – TitleFull: Insight: Non-Destructive Testing & Condition Monitoring Type: main |
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