Prognostics: Design, Implementation, and Challenges
| dc.contributor.author | Skaf, Zakwan | |
| dc.date.accessioned | 2016-08-01T15:44:02Z | |
| dc.date.available | 2016-08-01T15:44:02Z | |
| dc.date.issued | 2015-09-30 | |
| dc.description.abstract | Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life (RUL) of a system. It is also a key technology for an integrated vehicle health management (IVHM) system that leads to improved safety and reliability. A vast amount of research has been presented in the literature to develop prognostics models that are able to predict a system’s RUL. These models can be broadly categorised into experience-based models, data-driven models and physics-based models. Therefore, careful consideration needs to be given to selecting which prognostics model to take forward and apply for each real application. Currently, developing reliable prognostics models in real life is challenging for various reasons, such as the design complexity associated with a system, the high uncertainty and its propagation in the degradation, system level prognostics, the evaluation framework and a lack of prognostics standards. This paper is written with the aim to bring forth the challenges and opportunities for developing prognostics models for complex systems and making researchers aware of these challenges and opportunities. | en_UK |
| dc.identifier.citation | Zakwan Skaf. Prognostics: Design, Implementation, and Challenges. 12th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, 9-11 June 2015, Oxford, UK. | en_UK |
| dc.identifier.isbn | 9781510807129 | |
| dc.identifier.uri | http://www.proceedings.com/26860.html | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/10206 | |
| dc.language.iso | en | en_UK |
| dc.publisher | Curran Associates | en_UK |
| dc.rights | Attribution-Non-Commercial 3.0 Unported (CC BY-NC 3.0) You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: Non-Commercial — You may not use the material for commercial purposes. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. | |
| dc.title | Prognostics: Design, Implementation, and Challenges | en_UK |
| dc.type | Conference paper | en_UK |
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