A simple state-based prognostic model for filter clogging

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dc.contributor.authorSkaf, Zakwan
dc.contributor.authorEker, Ömer Faruk
dc.contributor.authorJennions, Ian K.
dc.date.accessioned2016-07-29T13:11:06Z
dc.date.available2016-07-29T13:11:06Z
dc.date.issued2015-10-27
dc.description.abstractIn today's maintenance planning, fuel filters are replaced or cleaned on a regular basis. Monitoring and implementation of prognostics on filtration system have the potential to avoid costs and increase safety. Prognostics is a fundamental technology within Integrated Vehicle Health Management (IVHM). Prognostic models can be categorised into three major categories: 1) Physics-based models 2) Data-driven models 3) Experience-based models. One of the challenges in the progression of the clogging filter failure is the inability to observe the natural clogging filter failure due to time constraint. This paper presents a simple solution to collect data for a clogging filter failure. Also, it represents a simple state-based prognostic with duration information (SSPD) method that aims to detect and forecast clogging of filter in a laboratory based fuel rig system. The progression of the clogging filter failure is created unnaturally. The degradation level is divided into several groups. Each group is defined as a state in the failure progression of clogging filter. Then, the data is collected to create the clogging filter progression states unnaturally. The SSPD method consists of three steps: clustering, clustering evaluation, and remaining useful life (RUL) estimation. Prognosis results show that the SSPD method is able to predicate the RUL of the clogging filter accurately.en_UK
dc.identifier.citationZakwan Skaf, Omer F. Eker and Ian K. Jennions. A simple state-based prognostic model for filter clogging. Proceedings of the 4th International Conference on Through-life Engineering ServicesA Simple State-Based Prognostic Model for Filter Clogging, Procedia CIRP, Volume 38, 2015, Pages 177-182en_UK
dc.identifier.issn2212-8271
dc.identifier.urihttp://dx.doi.org/10.1016/j.procir.2015.08.094
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/10194
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Attribution-Non-Commercial-No Derivs 4.0 Unported (CC BY-NC-ND 4.0). You are free to: Share — copy and redistribute the material in any medium or format. 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 Derivatives — If you remix, transform, or build upon the material, you may not distribute the modified material. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
dc.subjectPrognosticsen_UK
dc.subjectRemaining Useful Lifeen_UK
dc.subjectData Collectionen_UK
dc.subjectFilter Cloggingen_UK
dc.titleA simple state-based prognostic model for filter cloggingen_UK
dc.typeArticleen_UK

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