Predictive maintenance modelling for through-life engineering services
| dc.contributor.author | Okoh, Caxton | |
| dc.contributor.author | Roy, Rajkumar | |
| dc.contributor.author | Mehnen, Jorn | |
| dc.date.accessioned | 2017-10-18T13:22:48Z | |
| dc.date.available | 2017-10-18T13:22:48Z | |
| dc.date.issued | 2017-03-02 | |
| dc.description.abstract | Predictive maintenance needs to forecast the numbers of rejections at any overhaul point before any failure occurs in order to accurately and proactively take adequate maintenance action. In healthcare, prediction has been applied to foretell when and how to administer medication to improve the health condition of the patient. The same is true for maintenance where the application of prognostics can help make better decisions. In this paper, an overview of prognostic maintenance strategies is presented. The proposed data-driven prognostics approach employs a statistical technique of (i) the parameter estimation methods of the time-to-failure data to predict the relevant statistical model parameters and (ii) prognostics modelling incorporating the reliability Weibull Cumulative Distribution Function to predict part rejection, replacement, and reuse. The analysis of the modelling uses synthetic data validated by industry domain experts. The outcome of the prediction can further proffer solution to designers, manufacturers and operators of industrial product-service systems. The novelty in this paper is the development of the through-life performance approach. The approach ascertains when the system needs to undergo maintenance, repair and overhaul before failure occurs. | en_UK |
| dc.identifier.citation | C. Okoh, R. Roy, J. Mehnen, Predictive Maintenance Modelling for Through-Life Engineering Services, Procedia CIRP, Volume 59, 2017, Pages 196-201 | en_UK |
| dc.identifier.issn | 2212-8271 | |
| dc.identifier.uri | https://doi.org/10.1016/j.procir.2016.09.033 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/12658 | |
| dc.language.iso | en | en_UK |
| dc.publisher | Elsevier | en_UK |
| dc.rights | 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.subject | Through-life engineering services | en_UK |
| dc.subject | maintenance strategies | en_UK |
| dc.subject | data-driven prediction | en_UK |
| dc.subject | predictive modelling | en_UK |
| dc.subject | parameter estimation | en_UK |
| dc.title | Predictive maintenance modelling for through-life engineering services | en_UK |
| dc.type | Article | en_UK |