A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model
Prognostics aims to predict the remaining useful life (RUL) of an in-service system based on its degradation data. Existing methods, such as artificial neural networks (ANNs) and their variations, often face challenges in real-world applications due to their complexity and the lack of sufficient dat...
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2024-12-01
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| author | Dawn An |
| author_facet | Dawn An |
| author_sort | Dawn An |
| collection | DOAJ |
| description | Prognostics aims to predict the remaining useful life (RUL) of an in-service system based on its degradation data. Existing methods, such as artificial neural networks (ANNs) and their variations, often face challenges in real-world applications due to their complexity and the lack of sufficient data. In this paper, a practical prognostic method is proposed, based on the stepwise linear approximation of nonlinear degradation behavior, to simplify the prognostic process while significantly reducing computational costs and maintaining high accuracy. The proposed approach is validated using synthetic data generated at different noise levels, with 100 data sets tested at each level, and compared against a typical ANN method. The results demonstrate that the proposed method consistently outperforms the ANN in terms of accuracy and robustness, while remarkably reducing computational time by a factor of 50 to 60, making it a promising solution for real-world applications. |
| format | Article |
| id | doaj-art-db098208c40c458192928a9bcc64b98f |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-db098208c40c458192928a9bcc64b98f2025-01-10T13:14:58ZengMDPI AGApplied Sciences2076-34172024-12-0115126610.3390/app15010266A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation ModelDawn An0Advanced Mobility System Group, Korea Institute of Industrial Technology, Daegu 42994, Republic of KoreaPrognostics aims to predict the remaining useful life (RUL) of an in-service system based on its degradation data. Existing methods, such as artificial neural networks (ANNs) and their variations, often face challenges in real-world applications due to their complexity and the lack of sufficient data. In this paper, a practical prognostic method is proposed, based on the stepwise linear approximation of nonlinear degradation behavior, to simplify the prognostic process while significantly reducing computational costs and maintaining high accuracy. The proposed approach is validated using synthetic data generated at different noise levels, with 100 data sets tested at each level, and compared against a typical ANN method. The results demonstrate that the proposed method consistently outperforms the ANN in terms of accuracy and robustness, while remarkably reducing computational time by a factor of 50 to 60, making it a promising solution for real-world applications.https://www.mdpi.com/2076-3417/15/1/266prognosticsremaining useful lifelinear approximationartificial neural networks |
| spellingShingle | Dawn An A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model Applied Sciences prognostics remaining useful life linear approximation artificial neural networks |
| title | A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model |
| title_full | A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model |
| title_fullStr | A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model |
| title_full_unstemmed | A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model |
| title_short | A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model |
| title_sort | practical prognostics method based on stepwise linear approximation of a nonlinear degradation model |
| topic | prognostics remaining useful life linear approximation artificial neural networks |
| url | https://www.mdpi.com/2076-3417/15/1/266 |
| work_keys_str_mv | AT dawnan apracticalprognosticsmethodbasedonstepwiselinearapproximationofanonlineardegradationmodel AT dawnan practicalprognosticsmethodbasedonstepwiselinearapproximationofanonlineardegradationmodel |