Multi-Objective Hybrid Artificial Intelligence Approach for Fault Diagnosis of Aerospace Systems
Due to the space environment’s hazards and challenges, aerospace systems are continuously exposed to many failures, such as the degradation of the subsystem performance, sensor faults, connection loss, or equipment damage. Therefore, the effective fault diagnosis for detecting and identif...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9373304/ |
| _version_ | 1818726177485881344 |
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| author | Dalia Ezzat Aboul Ella Hassanien Ashraf Darwish Mohamed Yahia Ayman Ahmed Sara Abdelghafar |
| author_facet | Dalia Ezzat Aboul Ella Hassanien Ashraf Darwish Mohamed Yahia Ayman Ahmed Sara Abdelghafar |
| author_sort | Dalia Ezzat |
| collection | DOAJ |
| description | Due to the space environment’s hazards and challenges, aerospace systems are continuously exposed to many failures, such as the degradation of the subsystem performance, sensor faults, connection loss, or equipment damage. Therefore, the effective fault diagnosis for detecting and identifying any failures or unusual behaviors can be recognized as the fundamental and critical role of aerospace systems’ predictive health management process. This paper proposes a novel fault diagnosis approach using Deep Learning (DL) technique. DL has recently become a popular approach in artificial intelligence (AI) due to its supremacy in accuracy and fast inference with the huge amount of data and highest order network structures. The proposed approach consists of two main phases; the feature selection phase by Binary Grasshopper Optimization Algorithm (BGOA), and the learning and prediction phase by Artificial Neural Networks (ANNs) with voting ensemble method. The proposed approach named BGOA-EANNs has been validated and evaluated its efficacy by comparing two existing diagnosis techniques using two types of aerospace health diagnosis datasets; satellite power system and aircraft engines. The experimental results demonstrated the effectiveness and superiority of the proposed approach. |
| first_indexed | 2024-12-17T21:54:04Z |
| format | Article |
| id | doaj.art-11835401239f4c1cb29627275d046903 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T21:54:04Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-11835401239f4c1cb29627275d0469032022-12-21T21:31:10ZengIEEEIEEE Access2169-35362021-01-019417174173010.1109/ACCESS.2021.30649769373304Multi-Objective Hybrid Artificial Intelligence Approach for Fault Diagnosis of Aerospace SystemsDalia Ezzat0Aboul Ella Hassanien1Ashraf Darwish2Mohamed Yahia3Ayman Ahmed4Sara Abdelghafar5https://orcid.org/0000-0003-3590-4109Faculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptFaculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptFaculty of Science, Helwan University, Cairo, EgyptSpace Division, National Authority for Remote Sensing and Space Sciences, El-Nozha El-Gedida, EgyptSpace Division, National Authority for Remote Sensing and Space Sciences, El-Nozha El-Gedida, EgyptComputer Science Division, Faculty of Science, Al Azhar University, Cairo, EgyptDue to the space environment’s hazards and challenges, aerospace systems are continuously exposed to many failures, such as the degradation of the subsystem performance, sensor faults, connection loss, or equipment damage. Therefore, the effective fault diagnosis for detecting and identifying any failures or unusual behaviors can be recognized as the fundamental and critical role of aerospace systems’ predictive health management process. This paper proposes a novel fault diagnosis approach using Deep Learning (DL) technique. DL has recently become a popular approach in artificial intelligence (AI) due to its supremacy in accuracy and fast inference with the huge amount of data and highest order network structures. The proposed approach consists of two main phases; the feature selection phase by Binary Grasshopper Optimization Algorithm (BGOA), and the learning and prediction phase by Artificial Neural Networks (ANNs) with voting ensemble method. The proposed approach named BGOA-EANNs has been validated and evaluated its efficacy by comparing two existing diagnosis techniques using two types of aerospace health diagnosis datasets; satellite power system and aircraft engines. The experimental results demonstrated the effectiveness and superiority of the proposed approach.https://ieeexplore.ieee.org/document/9373304/Aerospace systemsaircraft enginesbinary grasshopper optimizerdeep learningfault diagnosisprognostic health management |
| spellingShingle | Dalia Ezzat Aboul Ella Hassanien Ashraf Darwish Mohamed Yahia Ayman Ahmed Sara Abdelghafar Multi-Objective Hybrid Artificial Intelligence Approach for Fault Diagnosis of Aerospace Systems IEEE Access Aerospace systems aircraft engines binary grasshopper optimizer deep learning fault diagnosis prognostic health management |
| title | Multi-Objective Hybrid Artificial Intelligence Approach for Fault Diagnosis of Aerospace Systems |
| title_full | Multi-Objective Hybrid Artificial Intelligence Approach for Fault Diagnosis of Aerospace Systems |
| title_fullStr | Multi-Objective Hybrid Artificial Intelligence Approach for Fault Diagnosis of Aerospace Systems |
| title_full_unstemmed | Multi-Objective Hybrid Artificial Intelligence Approach for Fault Diagnosis of Aerospace Systems |
| title_short | Multi-Objective Hybrid Artificial Intelligence Approach for Fault Diagnosis of Aerospace Systems |
| title_sort | multi objective hybrid artificial intelligence approach for fault diagnosis of aerospace systems |
| topic | Aerospace systems aircraft engines binary grasshopper optimizer deep learning fault diagnosis prognostic health management |
| url | https://ieeexplore.ieee.org/document/9373304/ |
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