Comparison of Computational Prognostic Methods for Complex Systems Under Dynamic Regimes: A Review of Perspectives
| Autor: | Jeffrey Alun Jones, Jane Marshall, Oguz Bektas |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Applied Mathematics Complex system 02 engineering and technology 01 natural sciences Data science Computer Science Applications Variety (cybernetics) 010101 applied mathematics 0202 electrical engineering electronic engineering information engineering Key (cryptography) Prognostics 020201 artificial intelligence & image processing 0101 mathematics |
| Zdroj: | Archives of Computational Methods in Engineering. 27:999-1011 |
| ISSN: | 1886-1784 1134-3060 |
| DOI: | 10.1007/s11831-019-09339-7 |
| Popis: | Complex systems are expected to play a key role in the progress of Prognostics Health Management but the breadth of technologies that will highlight gaps in the dynamic regimes are expected to become more prominent and likely more challenging in the future. The design and implementation of sophisticated computational algorithms have become a critical aspect to solve problems in many prognostic applications for multiple regimes. In addition to a wide variety of conventional computational and cognitive paradigms such as machine learning and data mining fields, specific applications in prognostics have led to a wealth of newly proposed methods and techniques. This paper reviews practices for modeling prognostics and remaining useful life applications in complex systems working under multiple operational regimes. An analysis is provided to compare and combine the findings of previously published studies in the literature, and it assesses the effectiveness of techniques for different stages of prognostic development. The paper concludes with some speculations on the likely advances in fusion of advanced methods for case specific modeling. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink