Model-Assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis
A diagnostic method for gas-path faults of turbofan engines, relying on a Probabilistic Neural Network (PNN) coupled with a thermodynamic model of the engine, is presented. The novel aspect of the method is that its training information is generated dynamically by an accompanying Engine Performance...
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| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/11/11/913 |
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| author | Christoforos Romesis Nikolaos Aretakis Konstantinos Mathioudakis |
| author_facet | Christoforos Romesis Nikolaos Aretakis Konstantinos Mathioudakis |
| author_sort | Christoforos Romesis |
| collection | DOAJ |
| description | A diagnostic method for gas-path faults of turbofan engines, relying on a Probabilistic Neural Network (PNN) coupled with a thermodynamic model of the engine, is presented. The novel aspect of the method is that its training information is generated dynamically by an accompanying Engine Performance Model. In the proposed approach, the PNN efficiently addresses the first step of a diagnostic process (i.e., detection of the faulty component at the current operating point), while with the aid of an adaptive engine model, the fault is then further isolated and identified. A description of the proposed method and training aspects of the PNN are presented. The method is applied to the case of a mixed-flow turbofan engine to diagnose common gas-path faults in compressors and turbines (i.e., fouling, FOD, erosion, and tip clearance). Its performance is evaluated using realistic fault data that may be acquired at various operating conditions within a flight envelope. |
| format | Article |
| id | doaj-art-525cfe5b6cd44f528851ee14412dbdff |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-525cfe5b6cd44f528851ee14412dbdff2025-08-20T01:53:52ZengMDPI AGAerospace2226-43102024-11-01111191310.3390/aerospace11110913Model-Assisted Probabilistic Neural Networks for Effective Turbofan Fault DiagnosisChristoforos Romesis0Nikolaos Aretakis1Konstantinos Mathioudakis2Laboratory of Thermal Turbomachines, School of Mechanical Engineering, National Technical University of Athens, 15710 Athens, GreeceLaboratory of Thermal Turbomachines, School of Mechanical Engineering, National Technical University of Athens, 15710 Athens, GreeceLaboratory of Thermal Turbomachines, School of Mechanical Engineering, National Technical University of Athens, 15710 Athens, GreeceA diagnostic method for gas-path faults of turbofan engines, relying on a Probabilistic Neural Network (PNN) coupled with a thermodynamic model of the engine, is presented. The novel aspect of the method is that its training information is generated dynamically by an accompanying Engine Performance Model. In the proposed approach, the PNN efficiently addresses the first step of a diagnostic process (i.e., detection of the faulty component at the current operating point), while with the aid of an adaptive engine model, the fault is then further isolated and identified. A description of the proposed method and training aspects of the PNN are presented. The method is applied to the case of a mixed-flow turbofan engine to diagnose common gas-path faults in compressors and turbines (i.e., fouling, FOD, erosion, and tip clearance). Its performance is evaluated using realistic fault data that may be acquired at various operating conditions within a flight envelope.https://www.mdpi.com/2226-4310/11/11/913gas turbine diagnosisartificial neural networksgas-path faultsgas turbine performance modeling |
| spellingShingle | Christoforos Romesis Nikolaos Aretakis Konstantinos Mathioudakis Model-Assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis Aerospace gas turbine diagnosis artificial neural networks gas-path faults gas turbine performance modeling |
| title | Model-Assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis |
| title_full | Model-Assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis |
| title_fullStr | Model-Assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis |
| title_full_unstemmed | Model-Assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis |
| title_short | Model-Assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis |
| title_sort | model assisted probabilistic neural networks for effective turbofan fault diagnosis |
| topic | gas turbine diagnosis artificial neural networks gas-path faults gas turbine performance modeling |
| url | https://www.mdpi.com/2226-4310/11/11/913 |
| work_keys_str_mv | AT christoforosromesis modelassistedprobabilisticneuralnetworksforeffectiveturbofanfaultdiagnosis AT nikolaosaretakis modelassistedprobabilisticneuralnetworksforeffectiveturbofanfaultdiagnosis AT konstantinosmathioudakis modelassistedprobabilisticneuralnetworksforeffectiveturbofanfaultdiagnosis |