Presentation at Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021 organized by the scientific research group in Egypt with Collaboration with Faculty of Computers and AI, Cairo University and the Chinese University in Egypt

1. Multi-Objective Hybrid Artificial
Intelligence Approach for Fault Diagnosis
of Aerospace Systems
Dr. Sara Abdelghafar Ahmed
Computer Science, Candian International College (CIC)
Senior Member of Scientific Research Group in Egypt (SRGE)
sara.abdelghafar@yahoo.com
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Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021

2. Agenda
• Introduction
1
• Problem Definition
2
• Proposed Approach
3
• Dataset Description
4
• Experimental Results
5
• Conclusion and Future Work
6
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Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021

3. Introduction
 Due to the harsh and challenging space
environment, it is virtually impossible to
eradicate the risk of faults.
 Faults of aerospace may indicate the connection
loss, equipment damage, degradation of the
subsystem performance, or complete
interruption of the system's capability to
perform required functions.
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Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021

4. Introduction
 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.
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Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021

5. Problem Definition
 The fault diagnosis based on data-driven methods can be
seen as the classification problem (Binary or Multi-
classification).
 But, the inherent characteristics of aerospace systems data
(high dimensionality, multimodality, outliers, missing
data,..) make the traditional methods are not sufficiently
sufficient to analyze and derive the information contained
in these characteristics.
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Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021

6. Proposed Approach
• The proposed approach consists of two
primary phases.
• The first phase is the feature selection
phase, BGOA with KNN classifier is
employed in the feature selection
phase as a wrapper method that
explores the optimal subset of features.
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Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021

7. Proposed Approach
• The second phase is the training and prediction
phase, which is based on the Ensemble
Learning method used to combine the three
proposed classifier models to obtain more
accurate outputs.
• The three models are built using ANNs that are
the simplest class of DL architectures and
have great success in analyzing tabular
datasets.
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Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021

8. Dataset Description
• The first dataset is benchmark PHM08
prognostic challenge dataset that is
available in NASA's data repository
• The dataset includes measured
parameters that represent the
performance degradation till failure
occurs of aircraft engine's.
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Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021

9. Dataset Description
• The second one is the power
subsystem in GEO satellite dataset.
The collected dataset representing
the performance and state
monitoring of battery and solar
arrays that can reflect the power
system's status.
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Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021

10. Experimental results with
PHM08 dataset
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11. Experimental results with
PHM08 dataset.
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12. Experimental results with
PHM08 dataset.
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Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021

13. Experimental results with GEO
Power Subsystem dataset
Accuracy of the proposed approach
BGOA-EANNs (%)
Accuracy of the DNDTRS approach
(%)
99% 97.03%
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Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021

14. • https://ieeexplore.ieee.org/document/9373304
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15. Conclusion
 This paper contributes to aerospace systems' prognostic health management process
that requires fault diagnosis to detect any failures or unusual behaviors to improve
system safety and increase efficiency and reliability.
 A novel fault diagnosis approach for the aerospace system is proposed mainly based
on BGOA and EANNs.
 The proposed approach is evaluated against two existing fault diagnosis techniques,
using two types of the aerospace dataset; aircraft engines and satellite power system.
 The experimental results demonstrated the effectiveness of the proposed approach
and proved its efficiency.
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Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021

16. Future Work
 In future work, fault diagnosis for multivariate samples of more different types
of aerospace applications with different and multiple modes and different real
conditions of the space environment will be extensively studied.
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Advanced Intelligent Systems for Sustainable Development (AISSD 2021) 20-22 August 2021

17. Acknowledgment