Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 11-24 |
| Number of pages | 13 |
| Journal | Annals of Operations Research |
| Volume | 91 |
| DOIs | |
| Publication status | Published - 1999 |
Fingerprint
Keywords
- reliability growth
- innovation
- data analysis
- Bayesian statistics
- importance measures
- Monte Carlo Markov chains
Cite this
}
Achieving growth in reliability. / Ansell, J.I.; Walls, L.A.; Quigley, J.L.
In: Annals of Operations Research, Vol. 91, 1999, p. 11-24.Research output: Contribution to journal › Article
TY - JOUR
T1 - Achieving growth in reliability
AU - Ansell, J.I.
AU - Walls, L.A.
AU - Quigley, J.L.
PY - 1999
Y1 - 1999
N2 - There exists a considerable literature on reliability growth modelling. Recently somecommon models have been extended to encompass innovation. They assume the time of aninnovation is known and that there is a coincidental improvement in performance. However,despite such developments, it remains that most models do not fully address the engineeringconcerns as they do not capture the underlying physical processes and they tend to beoptimistic about ensuing performance. This paper aims to address these issues by specifyinga general framework for reliability growth that supports more effective modelling. Further,we develop a strategy for using such models proactively during development to facilitatemeaningful improvements in reliability performance.
AB - There exists a considerable literature on reliability growth modelling. Recently somecommon models have been extended to encompass innovation. They assume the time of aninnovation is known and that there is a coincidental improvement in performance. However,despite such developments, it remains that most models do not fully address the engineeringconcerns as they do not capture the underlying physical processes and they tend to beoptimistic about ensuing performance. This paper aims to address these issues by specifyinga general framework for reliability growth that supports more effective modelling. Further,we develop a strategy for using such models proactively during development to facilitatemeaningful improvements in reliability performance.
KW - reliability growth
KW - innovation
KW - data analysis
KW - Bayesian statistics
KW - importance measures
KW - Monte Carlo Markov chains
UR - http://dx.doi.org/10.1023/A:1018985303897
U2 - 10.1023/A:1018985303897
DO - 10.1023/A:1018985303897
M3 - Article
VL - 91
SP - 11
EP - 24
JO - Annals of Operations Research
JF - Annals of Operations Research
SN - 0254-5330
ER -