Life Time Prediction of an Electromagnet Relay using Clustering based Principal Component Analysis with Hybrid Deep Learning Model
The estimation of the electromagnet relay remaining useful life is highly crucial to maintain reliability and avoid unscheduled breakdowns in various applications. The objective of this research work will be to design a model with much higher precision and efficiency utilizing PCA coupled with a hy...
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Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)
2024-12-01
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| Series: | Journal of Applied Engineering and Technological Science |
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| Online Access: | https://journal.yrpipku.com/index.php/jaets/article/view/5891 |
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| author | T. Maris Murugan C. Reeda Lenus S. Sridharan A. Malligarjun |
| author_facet | T. Maris Murugan C. Reeda Lenus S. Sridharan A. Malligarjun |
| author_sort | T. Maris Murugan |
| collection | DOAJ |
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The estimation of the electromagnet relay remaining useful life is highly crucial to maintain reliability and avoid unscheduled breakdowns in various applications. The objective of this research work will be to design a model with much higher precision and efficiency utilizing PCA coupled with a hybrid deep learning architecture of Bi-LSTM along with Bi-GRU. The C-MAPSS dataset was of reduced dimensionality, since PCA has been applied to eliminate data redundancy while retaining crucial characteristics, and then K-means clustering is applied to classify the data; afterwards, the Bi-LSTM and Bi-GRU models are implemented for RUL relay prediction. The proposed method in comparison with typical deep learning models has a Mean Absolute Error of 0.021 and an R² of 0.996. Results developed reflect how the model can produce some very powerful prediction, however; what it really shows is great potential for this approach with respect to predictive maintenance of electromagnet relays. PCA may well amalgamate with Bi-LSTM and Bi-GRU models to achieve great scalability according to the maintenance engineering, which offers practical applications in improving the lifetime of the electromagnet relays.
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| format | Article |
| id | doaj-art-e4a8be17859e4fd2a7bd597659784752 |
| institution | OA Journals |
| issn | 2715-6087 2715-6079 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI) |
| record_format | Article |
| series | Journal of Applied Engineering and Technological Science |
| spelling | doaj-art-e4a8be17859e4fd2a7bd5976597847522025-08-20T02:37:20ZengYayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)Journal of Applied Engineering and Technological Science2715-60872715-60792024-12-016110.37385/jaets.v6i1.5891Life Time Prediction of an Electromagnet Relay using Clustering based Principal Component Analysis with Hybrid Deep Learning Model T. Maris Murugan0C. Reeda Lenus1S. Sridharan2A. Malligarjun3Associate Professor, Department of Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Erode, Tamilnadu, India.Assistant Professor, Department of Physics, S.A. Engineering College, Thiruverkadu, Tamil Nadu, India.Associate Professor, Department of Electrical and Electronics Engineering, St. Joseph's College of Engineering, Chennai, Tamil Nadu, IndiaScholar, Department of Artificial Intelligence and Data Science, Erode Sengunthar Engineering College, Erode, Tamilnadu, India. The estimation of the electromagnet relay remaining useful life is highly crucial to maintain reliability and avoid unscheduled breakdowns in various applications. The objective of this research work will be to design a model with much higher precision and efficiency utilizing PCA coupled with a hybrid deep learning architecture of Bi-LSTM along with Bi-GRU. The C-MAPSS dataset was of reduced dimensionality, since PCA has been applied to eliminate data redundancy while retaining crucial characteristics, and then K-means clustering is applied to classify the data; afterwards, the Bi-LSTM and Bi-GRU models are implemented for RUL relay prediction. The proposed method in comparison with typical deep learning models has a Mean Absolute Error of 0.021 and an R² of 0.996. Results developed reflect how the model can produce some very powerful prediction, however; what it really shows is great potential for this approach with respect to predictive maintenance of electromagnet relays. PCA may well amalgamate with Bi-LSTM and Bi-GRU models to achieve great scalability according to the maintenance engineering, which offers practical applications in improving the lifetime of the electromagnet relays. https://journal.yrpipku.com/index.php/jaets/article/view/5891Electromagnet RelayRemaining Useful LifeBidirectional Long Short Memory with Bidirectional Gated Recurrent UnitPrincipal Component Analysis |
| spellingShingle | T. Maris Murugan C. Reeda Lenus S. Sridharan A. Malligarjun Life Time Prediction of an Electromagnet Relay using Clustering based Principal Component Analysis with Hybrid Deep Learning Model Journal of Applied Engineering and Technological Science Electromagnet Relay Remaining Useful Life Bidirectional Long Short Memory with Bidirectional Gated Recurrent Unit Principal Component Analysis |
| title | Life Time Prediction of an Electromagnet Relay using Clustering based Principal Component Analysis with Hybrid Deep Learning Model |
| title_full | Life Time Prediction of an Electromagnet Relay using Clustering based Principal Component Analysis with Hybrid Deep Learning Model |
| title_fullStr | Life Time Prediction of an Electromagnet Relay using Clustering based Principal Component Analysis with Hybrid Deep Learning Model |
| title_full_unstemmed | Life Time Prediction of an Electromagnet Relay using Clustering based Principal Component Analysis with Hybrid Deep Learning Model |
| title_short | Life Time Prediction of an Electromagnet Relay using Clustering based Principal Component Analysis with Hybrid Deep Learning Model |
| title_sort | life time prediction of an electromagnet relay using clustering based principal component analysis with hybrid deep learning model |
| topic | Electromagnet Relay Remaining Useful Life Bidirectional Long Short Memory with Bidirectional Gated Recurrent Unit Principal Component Analysis |
| url | https://journal.yrpipku.com/index.php/jaets/article/view/5891 |
| work_keys_str_mv | AT tmarismurugan lifetimepredictionofanelectromagnetrelayusingclusteringbasedprincipalcomponentanalysiswithhybriddeeplearningmodel AT creedalenus lifetimepredictionofanelectromagnetrelayusingclusteringbasedprincipalcomponentanalysiswithhybriddeeplearningmodel AT ssridharan lifetimepredictionofanelectromagnetrelayusingclusteringbasedprincipalcomponentanalysiswithhybriddeeplearningmodel AT amalligarjun lifetimepredictionofanelectromagnetrelayusingclusteringbasedprincipalcomponentanalysiswithhybriddeeplearningmodel |