Multi-Source Information Fusion Diagnosis Method for Aero Engine
Aero engines are complex coupled systems in which faults in one subsystem can propagate and affect the performance of others. Relying on single-source performance parameters is often insufficient for accurately assessing component degradation. Although multi-source fusion diagnosis methods, such as...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/5083 |
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| author | Kai Yin Yawen Shen Yifan Chen Huisheng Zhang |
| author_facet | Kai Yin Yawen Shen Yifan Chen Huisheng Zhang |
| author_sort | Kai Yin |
| collection | DOAJ |
| description | Aero engines are complex coupled systems in which faults in one subsystem can propagate and affect the performance of others. Relying on single-source performance parameters is often insufficient for accurately assessing component degradation. Although multi-source fusion diagnosis methods, such as those based on Bayesian networks, have been widely applied, their diagnostic performance remains limited when prior knowledge is scarce. To address this challenge, this paper proposes a multi-source information fusion diagnosis method for aero engine fault detection based on Dempster–Shafer (D-S) evidence theory. Data from gas path and vibration subsystems are separately processed to extract fault features, and a decision-level fusion strategy is employed to achieve comprehensive diagnoses. A case study based on real operational data from a two-shaft aero engine demonstrates that the proposed method significantly improves diagnostic performance. Specifically, the Bayesian-network-based fusion method achieves a diagnostic confidence of 87.2% without prior knowledge and 91.2% with prior knowledge incorporated, whereas D-S evidence theory attains a higher fault confidence of 99.6% without requiring any prior information. |
| format | Article |
| id | doaj-art-f5bb3e8e2ed040d59b1f49e272500a9b |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-f5bb3e8e2ed040d59b1f49e272500a9b2025-08-20T02:24:45ZengMDPI AGApplied Sciences2076-34172025-05-01159508310.3390/app15095083Multi-Source Information Fusion Diagnosis Method for Aero EngineKai Yin0Yawen Shen1Yifan Chen2Huisheng Zhang3AECC Commercial Aircraft Engine Co., Ltd., Shanghai 200241, ChinaAECC Commercial Aircraft Engine Co., Ltd., Shanghai 200241, ChinaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaAero engines are complex coupled systems in which faults in one subsystem can propagate and affect the performance of others. Relying on single-source performance parameters is often insufficient for accurately assessing component degradation. Although multi-source fusion diagnosis methods, such as those based on Bayesian networks, have been widely applied, their diagnostic performance remains limited when prior knowledge is scarce. To address this challenge, this paper proposes a multi-source information fusion diagnosis method for aero engine fault detection based on Dempster–Shafer (D-S) evidence theory. Data from gas path and vibration subsystems are separately processed to extract fault features, and a decision-level fusion strategy is employed to achieve comprehensive diagnoses. A case study based on real operational data from a two-shaft aero engine demonstrates that the proposed method significantly improves diagnostic performance. Specifically, the Bayesian-network-based fusion method achieves a diagnostic confidence of 87.2% without prior knowledge and 91.2% with prior knowledge incorporated, whereas D-S evidence theory attains a higher fault confidence of 99.6% without requiring any prior information.https://www.mdpi.com/2076-3417/15/9/5083information fusionaero enginemultiple fault featureBayesian networkD-S evidence theorydecision-level fusion |
| spellingShingle | Kai Yin Yawen Shen Yifan Chen Huisheng Zhang Multi-Source Information Fusion Diagnosis Method for Aero Engine Applied Sciences information fusion aero engine multiple fault feature Bayesian network D-S evidence theory decision-level fusion |
| title | Multi-Source Information Fusion Diagnosis Method for Aero Engine |
| title_full | Multi-Source Information Fusion Diagnosis Method for Aero Engine |
| title_fullStr | Multi-Source Information Fusion Diagnosis Method for Aero Engine |
| title_full_unstemmed | Multi-Source Information Fusion Diagnosis Method for Aero Engine |
| title_short | Multi-Source Information Fusion Diagnosis Method for Aero Engine |
| title_sort | multi source information fusion diagnosis method for aero engine |
| topic | information fusion aero engine multiple fault feature Bayesian network D-S evidence theory decision-level fusion |
| url | https://www.mdpi.com/2076-3417/15/9/5083 |
| work_keys_str_mv | AT kaiyin multisourceinformationfusiondiagnosismethodforaeroengine AT yawenshen multisourceinformationfusiondiagnosismethodforaeroengine AT yifanchen multisourceinformationfusiondiagnosismethodforaeroengine AT huishengzhang multisourceinformationfusiondiagnosismethodforaeroengine |