A Generic Framework for Prognostics of Complex Systems
In recent years, there has been an enormous increase in the amount of research in the field of prognostics and predictive maintenance for mechanical and electrical systems. Most of the existing approaches are tailored to one specific system. They do not provide a high degree of flexibility and often...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/9/12/839 |
| _version_ | 1797461975424303104 |
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| author | Marie Bieber Wim J. C. Verhagen |
| author_facet | Marie Bieber Wim J. C. Verhagen |
| author_sort | Marie Bieber |
| collection | DOAJ |
| description | In recent years, there has been an enormous increase in the amount of research in the field of prognostics and predictive maintenance for mechanical and electrical systems. Most of the existing approaches are tailored to one specific system. They do not provide a high degree of flexibility and often cannot be adaptively used on different systems. This can lead to years of research, knowledge, and expertise being put in the implementation of prognostics models without the capacity to estimate the remaining useful life of systems, either because of lack of data or data quality or simply because failure behaviour cannot be captured by data-driven models. To overcome this, in this paper we present an adaptive prognostic framework which can be applied to different systems while providing a way to assess whether or not it makes sense to put more time into the development of prognostic models for a system. The framework incorporates steps necessary for prognostics, including data pre-processing, feature extraction and machine learning algorithms for remaining useful life estimation. The framework is applied to two systems: a simulated turbofan engine dataset and an aircraft cooling unit dataset. The results show that the obtained accuracy of the remaining useful life estimates are comparable to what has been achieved in literature and highlight considerations for suitability assessment of systems data towards prognostics. |
| first_indexed | 2024-03-09T17:26:59Z |
| format | Article |
| id | doaj.art-76475e3da6cf4ba6b21f1d0bcd8ab85f |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-09T17:26:59Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-76475e3da6cf4ba6b21f1d0bcd8ab85f2023-11-24T12:38:53ZengMDPI AGAerospace2226-43102022-12-0191283910.3390/aerospace9120839A Generic Framework for Prognostics of Complex SystemsMarie Bieber0Wim J. C. Verhagen1Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The NetherlandsAerospace Engineering and Aviation, RMIT University, Carlton, VIC 3053, AustraliaIn recent years, there has been an enormous increase in the amount of research in the field of prognostics and predictive maintenance for mechanical and electrical systems. Most of the existing approaches are tailored to one specific system. They do not provide a high degree of flexibility and often cannot be adaptively used on different systems. This can lead to years of research, knowledge, and expertise being put in the implementation of prognostics models without the capacity to estimate the remaining useful life of systems, either because of lack of data or data quality or simply because failure behaviour cannot be captured by data-driven models. To overcome this, in this paper we present an adaptive prognostic framework which can be applied to different systems while providing a way to assess whether or not it makes sense to put more time into the development of prognostic models for a system. The framework incorporates steps necessary for prognostics, including data pre-processing, feature extraction and machine learning algorithms for remaining useful life estimation. The framework is applied to two systems: a simulated turbofan engine dataset and an aircraft cooling unit dataset. The results show that the obtained accuracy of the remaining useful life estimates are comparable to what has been achieved in literature and highlight considerations for suitability assessment of systems data towards prognostics.https://www.mdpi.com/2226-4310/9/12/839prognostics and health managementadaptive frameworkremaining useful life |
| spellingShingle | Marie Bieber Wim J. C. Verhagen A Generic Framework for Prognostics of Complex Systems Aerospace prognostics and health management adaptive framework remaining useful life |
| title | A Generic Framework for Prognostics of Complex Systems |
| title_full | A Generic Framework for Prognostics of Complex Systems |
| title_fullStr | A Generic Framework for Prognostics of Complex Systems |
| title_full_unstemmed | A Generic Framework for Prognostics of Complex Systems |
| title_short | A Generic Framework for Prognostics of Complex Systems |
| title_sort | generic framework for prognostics of complex systems |
| topic | prognostics and health management adaptive framework remaining useful life |
| url | https://www.mdpi.com/2226-4310/9/12/839 |
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