Recent developments in technology permit detailed descriptions of system performance to be collected and stored. Consequently, more data are available about the occurrence, or non-occurrence, of events across a range of classes through time. Typically this implies that reliability analysis has more information about the exposure history of a system within different classes of events. For highly reliable systems, there may be relatively few failure events. Thus there is a need to develop statistical inference to support reliability estimation when there is a low ratio of failures relative to event classes. In this paper we show how Empirical Bayes methods can be used to estimate a multivariate reliability function for a system by modelling the vector of times to realise each failure root cause.
|Number of pages||12|
|Publication status||Unpublished - 17 Jul 2007|
|Event||ISSAT international conference on modeling of complex systems and environments - Ho Chi Minh City, Vietnam|
Duration: 16 Jul 2007 → 18 Jul 2007
|Conference||ISSAT international conference on modeling of complex systems and environments|
|City||Ho Chi Minh City, Vietnam|
|Period||16/07/07 → 18/07/07|
- reliability growth model
- empirical Bayes
- engineering design process
Quigley, J. L., & Walls, L. A. (2007). Multivariate reliability modelling with empirical Bayes inference. 1-12. Paper presented at ISSAT international conference on modeling of complex systems and environments, Ho Chi Minh City, Vietnam, .