Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework
The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/8/2135 |
| _version_ | 1797538060479496192 |
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| author | Marcin Witczak Marcin Mrugalski Bogdan Lipiec |
| author_facet | Marcin Witczak Marcin Mrugalski Bogdan Lipiec |
| author_sort | Marcin Witczak |
| collection | DOAJ |
| description | The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository. |
| first_indexed | 2024-03-10T12:25:06Z |
| format | Article |
| id | doaj.art-6bf990c19eef4c3289993f0210725c38 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T12:25:06Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-6bf990c19eef4c3289993f0210725c382023-11-21T15:05:36ZengMDPI AGEnergies1996-10732021-04-01148213510.3390/en14082135Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno FrameworkMarcin Witczak0Marcin Mrugalski1Bogdan Lipiec2Institute of Control and Computation Engineering, University of Zielona Góra, 65-246 Zielona Góra, PolandInstitute of Control and Computation Engineering, University of Zielona Góra, 65-246 Zielona Góra, PolandInstitute of Control and Computation Engineering, University of Zielona Góra, 65-246 Zielona Góra, PolandThe paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.https://www.mdpi.com/1996-1073/14/8/2135remaining useful lifesoft computingTakagi–Sugenodegradation modelingprediction |
| spellingShingle | Marcin Witczak Marcin Mrugalski Bogdan Lipiec Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework Energies remaining useful life soft computing Takagi–Sugeno degradation modeling prediction |
| title | Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework |
| title_full | Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework |
| title_fullStr | Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework |
| title_full_unstemmed | Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework |
| title_short | Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework |
| title_sort | remaining useful life prediction of mosfets via the takagi sugeno framework |
| topic | remaining useful life soft computing Takagi–Sugeno degradation modeling prediction |
| url | https://www.mdpi.com/1996-1073/14/8/2135 |
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