Name
Abstract | ||
|---|---|---|
Machine failure is often an important factor in throughput of manufacturing systems. To simplify the inputs to the simulation model for complex machining and assembly lines, we have derived the Arrows classification method to group similar machines, where one model can be used to describe the breakdown times for all of the machines in the group and breakdown times of machines can be represented by finite mixture model distributions. The Two-Sample Cramér-von Mises statistic is used to measure the similarity of two sets of data. We evaluate the classification procedure by comparing the throughput of a simulation model when run with mixture models fitted to individual machine breakdown times; mixture models fitted to group breakdown times; and raw data. Details of the methods and results of the grouping processes will be presented, and will be demonstrated using an example. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/WSC.2007.4419638 | Winter Simulation Conference |
Keywords | Field | DocType |
classification procedure,simulation model,group breakdown time,arrows classification method,finite mixture model distribution,individual machine breakdown time,mixture model,breakdown time,raw data,machine failure,classification analysis,failure analysis,finite mixture model,group process,statistical analysis,digital signatures | Statistic,Computer science,Manufacturing systems,Simulation,Algorithm,Machining,Machine failure,Artificial intelligence,Throughput,Machine learning,Mixture model,Statistical analysis | Conference |
ISBN | Citations | PageRank |
1-4244-1306-0 | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lanting Lu | 1 | 2 | 1.48 |
| Christine S. M. Currie | 2 | 72 | 16.31 |
| Russell C. H. Cheng | 3 | 174 | 43.95 |
| John Ladbrook | 4 | 99 | 11.16 |