Probabilistic Monte-Carlo method for modelling and prediction of electronics component life

Simple item page
dc.contributor.authorSreenuch, T.
dc.contributor.authorAlghassi, Alireza
dc.contributor.authorPerinpanayagam, Suresh
dc.contributor.authorXie, Ye
dc.date.accessioned2016-04-07T12:45:47Z
dc.date.available2016-04-07T12:45:47Z
dc.date.issued2014-01-31
dc.description.abstractPower electronics are widely used in electric vehicles, railway locomotive and new generation aircrafts. Reliability of these components directly affect the reliability and performance of these vehicular platforms. In recent years, several research work about reliability, failure mode and aging analysis have been extensively carried out. There is a need for an efficient algorithm able to predict the life of power electronics component. In this paper, a probabilistic Monte-Carlo framework is developed and applied to predict remaining useful life of a component. Probability distributions are used to model the component’s degradation process. The modelling parameters are learned using Maximum Likelihood Estimation. The prognostic is carried out by the mean of simulation in this paper. Monte-Carlo simulation is used to propagate multiple possible degradation paths based on the current health state of the component. The remaining useful life and confident bounds are calculated by estimating mean, median and percentile descriptive statistics of the simulated degradation paths. Results from different probabilistic models are compared and their prognostic performances are evaluated.en_UK
dc.identifier.citationT. Sreenuch, A. Alghassi, S. Perinpanayagam and Y. Xie, Probabilistic Monte-Carlo method for modelling and prediction of electronics component life. International Journal of Advanced Computer Science and Applications(IJACSA), Volume 5 Issue 1, 2014. International Journal of Advanced Computer Science and Applications. Volume 5, Issue 1, pp96-104.en_UK
dc.identifier.issn2158-107X
dc.identifier.urihttp://dx.doi.org/10.14569/IJACSA.2014.050113
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/9813
dc.language.isoenen_UK
dc.publisherSAI Organizationen_UK
dc.rightsThis is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited. Attribution 4.0 International (CC BY 4.0) You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material for any purpose, even commercially. 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: 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.subjectMonte-Carlo Simulationen_UK
dc.subjectRemaining Useful Lifeen_UK
dc.titleProbabilistic Monte-Carlo method for modelling and prediction of electronics component lifeen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Probabilistic_Monte_Carlo_method_for_modelling_and_prediction_of_electronics_component_life-2014.pdf
Size:
731.75 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.79 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)