A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults
The deep learning diagnosis of aircraft engine-bearing faults enables cost-effective predictive maintenance while playing an important role in increasing the safety, reliability, and efficiency of aircraft operations. Because of highly dynamic and harsh operating conditions of this system, such mode...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/11/12/1089 |
| _version_ | 1797380235796152320 |
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| author | Tarek Berghout Toufik Bentrcia Wei Hong Lim Mohamed Benbouzid |
| author_facet | Tarek Berghout Toufik Bentrcia Wei Hong Lim Mohamed Benbouzid |
| author_sort | Tarek Berghout |
| collection | DOAJ |
| description | The deep learning diagnosis of aircraft engine-bearing faults enables cost-effective predictive maintenance while playing an important role in increasing the safety, reliability, and efficiency of aircraft operations. Because of highly dynamic and harsh operating conditions of this system, such modeling is challenging due to data complexity and drift, making it difficult to reveal failure patterns. As a result, the objective of this study is dual. To begin, a highly structured data preprocessing strategy ranging from extraction, denoising, outlier removal, scaling, and balancing is provided to solve data complexity that resides specifically in outliers, noise, and data imbalance problems. Gap statistics under k-means clustering are used to evaluate preprocessing results, providing a quantitative estimate of the ideal number of clusters and thereby enhancing data representations. This is the first time, to the best of authors’ knowledge, that such a criterion has been employed for an important step in a preliminary ground truth validation in supervised learning. Furthermore, to tackle data drift issues, long-short term memory (LSTM) adaptive learning features are used and subjected to a learning parameter improvement method utilizing recursive weights initialization (RWI) across several rounds. The strength of such methodology can be seen by application to realistic, extremely new, complex, and dynamic data collected from a real test-bench. Cross validation of a single LSTM layer model with only 10 neurons shows its ability to enhance classification performance by 7.7508% over state-of-the-art results, obtaining a classification accuracy of 92.03 ± 0.0849%, which is an exceptional performance in such a benchmark. |
| first_indexed | 2024-03-08T20:34:26Z |
| format | Article |
| id | doaj.art-01f5806d87fd46f0add3696d737ae5d1 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-08T20:34:26Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-01f5806d87fd46f0add3696d737ae5d12023-12-22T14:22:01ZengMDPI AGMachines2075-17022023-12-011112108910.3390/machines11121089A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing FaultsTarek Berghout0Toufik Bentrcia1Wei Hong Lim2Mohamed Benbouzid3Laboratory of Automation and Manufacturing Engineering, University of Batna 2, Batna 05000, AlgeriaLaboratory of Automation and Manufacturing Engineering, University of Batna 2, Batna 05000, AlgeriaFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, MalaysiaInstitut de Recherche Dupuy de Lôme (UMR CNRS 6027), University of Brest, 29238 Brest, FranceThe deep learning diagnosis of aircraft engine-bearing faults enables cost-effective predictive maintenance while playing an important role in increasing the safety, reliability, and efficiency of aircraft operations. Because of highly dynamic and harsh operating conditions of this system, such modeling is challenging due to data complexity and drift, making it difficult to reveal failure patterns. As a result, the objective of this study is dual. To begin, a highly structured data preprocessing strategy ranging from extraction, denoising, outlier removal, scaling, and balancing is provided to solve data complexity that resides specifically in outliers, noise, and data imbalance problems. Gap statistics under k-means clustering are used to evaluate preprocessing results, providing a quantitative estimate of the ideal number of clusters and thereby enhancing data representations. This is the first time, to the best of authors’ knowledge, that such a criterion has been employed for an important step in a preliminary ground truth validation in supervised learning. Furthermore, to tackle data drift issues, long-short term memory (LSTM) adaptive learning features are used and subjected to a learning parameter improvement method utilizing recursive weights initialization (RWI) across several rounds. The strength of such methodology can be seen by application to realistic, extremely new, complex, and dynamic data collected from a real test-bench. Cross validation of a single LSTM layer model with only 10 neurons shows its ability to enhance classification performance by 7.7508% over state-of-the-art results, obtaining a classification accuracy of 92.03 ± 0.0849%, which is an exceptional performance in such a benchmark.https://www.mdpi.com/2075-1702/11/12/1089aircraft enginedeep learningfault diagnosisinter-shaft bearinglong-short term memoryvibration |
| spellingShingle | Tarek Berghout Toufik Bentrcia Wei Hong Lim Mohamed Benbouzid A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults Machines aircraft engine deep learning fault diagnosis inter-shaft bearing long-short term memory vibration |
| title | A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults |
| title_full | A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults |
| title_fullStr | A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults |
| title_full_unstemmed | A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults |
| title_short | A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults |
| title_sort | neural network weights initialization approach for diagnosing real aircraft engine inter shaft bearing faults |
| topic | aircraft engine deep learning fault diagnosis inter-shaft bearing long-short term memory vibration |
| url | https://www.mdpi.com/2075-1702/11/12/1089 |
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