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Failure rate estimation in the case of zero failures

Authors: David J. Meade;

Failure rate estimation in the case of zero failures

Abstract

When a reliability test ends in zero units having failed, traditional reliability calculations suggest that the estimated failure rate is also zero, assuming an exponential distribution. However, this is not a realistic estimate of the failure rate, as it does not take into account the number of units on test. In such cases, a reasonable approach is to select a failure rate that makes the likelihood of observing zero failures equal to 50%. In other words, we select a failure rate that carries with it a high probability of observing zero failures for a given reliability test'. In this paper we review this methodology and demonstrate how it has been implemented at Advanced Micro Devices (AMD) through a user friendly EXCEL based software application. Customized software tools such as this have led to increased awareness, productivity, and accuracy in reliability calculations at AMD.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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