Life End Stock Need Estimation for Repairable Spare Components of Obsoleting Fleet by Simulation
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | Engineering Assets and Public Infrastructures in the Age of Digitalization |
| Subtitle of host publication | Proceedings of the 13th World Congress on Engineering Asset Management |
| Editors | Jayantha P. Liyanage, Joe Amadi-Echendu, Joseph Mathew |
| Publisher | Springer |
| Pages | 521-529 |
| Number of pages | 9 |
| ISBN (Electronic) | 978-3-030-48021-9 |
| ISBN (Print) | 978-3-030-48020-2 |
| DOIs | |
| Publication status | Published - 2020 |
| Publication type | A4 Article in a conference publication |
| Event | World Congress on Engineering Asset Management - Duration: 1 Jan 1900 → … |
Publication series
| Name | Lecture Notes in Mechanical Engineering |
|---|---|
| Publisher | Springer |
| ISSN (Print) | 2195-4356 |
| ISSN (Electronic) | 2195-4364 |
Conference
| Conference | World Congress on Engineering Asset Management |
|---|---|
| Period | 1/01/00 → … |
Abstract
Fleets of systems are often using common spare components. Occasionally when the manufacturing of some component will end a sufficient size of stock of components is needed in advance to cover the remaining life of the fleet. In this study the computer simulation model was built for making predictions about the fleet availability and spare part stock development concerning the whole life span of the fleet. The computer algorithm included algorithmically the corporation fleet running, repair, transport and storage rules. All event durations have been assumed to be stochastic that is random but evolving from the specific distribution. Failure probability distribution functions for the simulation were generated from the historical data of the components of the systems of the fleet. As a contribution, this study introduces a method of estimating failure probability density function for each failure count individually. This approach makes possible to capture the effect of actual repair process to the probability of the next component failure in simulation.