A deep learning-based approach for electrical equipment remaining useful life prediction
Abstract Electrical equipment maintenance is of vital importance to management companies. Efficient maintenance can significantly reduce business costs and avoid safety accidents caused by catastrophic equipment failures. In the current context, predictive maintenance (PdM) is becoming increasingly...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-07-01
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| Series: | Autonomous Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43684-022-00034-2 |
| _version_ | 1818018617854263296 |
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| author | Huibin Fu Ying Liu |
| author_facet | Huibin Fu Ying Liu |
| author_sort | Huibin Fu |
| collection | DOAJ |
| description | Abstract Electrical equipment maintenance is of vital importance to management companies. Efficient maintenance can significantly reduce business costs and avoid safety accidents caused by catastrophic equipment failures. In the current context, predictive maintenance (PdM) is becoming increasingly popular based on machine learning approaches, while its research on electrical equipment such as low-voltage contactors is in its infancy. The failure modes are mainly fusion welding and explosion, and a few are unable to switch on. In this study, a data-driven approach is proposed to predict the remaining useful life (RUL) of the low-voltage contactor. Firstly, the three-phase alternating voltage and current records the life of electrical equipment by tracking the number of times it has been operated. Secondly, the failure-relevant features are extracted by using the time domain, frequency domain, and wavelet methods. Then, a CNN-LSTM network is designed and used to train an electrical equipment RUL prediction model based on the extracted features. An experimental study based on ten datasets collected from low-voltage AC contactors reveals that the proposed method shows merits in comparison with the prevailing deep learning algorithms in terms of MAE and RMSE. |
| first_indexed | 2024-04-14T07:42:03Z |
| format | Article |
| id | doaj.art-24b7e2dfdb2149a68f4b2f4c75b2e277 |
| institution | Directory Open Access Journal |
| issn | 2730-616X |
| language | English |
| last_indexed | 2024-04-14T07:42:03Z |
| publishDate | 2022-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Autonomous Intelligent Systems |
| spelling | doaj.art-24b7e2dfdb2149a68f4b2f4c75b2e2772022-12-22T02:05:27ZengSpringerAutonomous Intelligent Systems2730-616X2022-07-012111210.1007/s43684-022-00034-2A deep learning-based approach for electrical equipment remaining useful life predictionHuibin Fu0Ying Liu1Department of Mechanical Engineering, School of Engineering, Cardiff UniversityDepartment of Mechanical Engineering, School of Engineering, Cardiff UniversityAbstract Electrical equipment maintenance is of vital importance to management companies. Efficient maintenance can significantly reduce business costs and avoid safety accidents caused by catastrophic equipment failures. In the current context, predictive maintenance (PdM) is becoming increasingly popular based on machine learning approaches, while its research on electrical equipment such as low-voltage contactors is in its infancy. The failure modes are mainly fusion welding and explosion, and a few are unable to switch on. In this study, a data-driven approach is proposed to predict the remaining useful life (RUL) of the low-voltage contactor. Firstly, the three-phase alternating voltage and current records the life of electrical equipment by tracking the number of times it has been operated. Secondly, the failure-relevant features are extracted by using the time domain, frequency domain, and wavelet methods. Then, a CNN-LSTM network is designed and used to train an electrical equipment RUL prediction model based on the extracted features. An experimental study based on ten datasets collected from low-voltage AC contactors reveals that the proposed method shows merits in comparison with the prevailing deep learning algorithms in terms of MAE and RMSE.https://doi.org/10.1007/s43684-022-00034-2Predictive maintenanceElectrical equipmentRemaining useful lifeMachine learningDeep learning |
| spellingShingle | Huibin Fu Ying Liu A deep learning-based approach for electrical equipment remaining useful life prediction Autonomous Intelligent Systems Predictive maintenance Electrical equipment Remaining useful life Machine learning Deep learning |
| title | A deep learning-based approach for electrical equipment remaining useful life prediction |
| title_full | A deep learning-based approach for electrical equipment remaining useful life prediction |
| title_fullStr | A deep learning-based approach for electrical equipment remaining useful life prediction |
| title_full_unstemmed | A deep learning-based approach for electrical equipment remaining useful life prediction |
| title_short | A deep learning-based approach for electrical equipment remaining useful life prediction |
| title_sort | deep learning based approach for electrical equipment remaining useful life prediction |
| topic | Predictive maintenance Electrical equipment Remaining useful life Machine learning Deep learning |
| url | https://doi.org/10.1007/s43684-022-00034-2 |
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