Providing Better Insights: Improved Life Analyses Using Degradation Testing

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Date
2013-11-01
Authors
Doganaksoy, Necip
Hahn, Gerald
Meeker, William
Meeker, William
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Altmetrics
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Statistics
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Statistics
Abstract

An earlier article emphasized the first of these advantages and provided extensive technical detail.2 Battery performance study Our case study concerns the development of a new battery consisting of electrochemical cells.3 A key application of the battery is to provide a backup source of power for uninterruptible power supplies. The battery was designed to carry the full load for up to 15 minutes to allow sufficient time to start the main backup power source (for example, a diesel generator). Because a battery is subjected to repeated charge and discharge cycling, it gradually loses its ability to hold power and, ultimately, fails. A lognormal distribution with constant shape parameter (standard deviation of log life) was used to model the battery lifetimes at each test condition.4 Table 1 shows the resulting estimated probability of failure at the nominal use condition (175°C and 135 watts/cell), as determined from the fitted model, after 1,000, 5,000, 10,000 and 20,000 cycles, and the associated (large sample theory) 95% confidence intervals around these estimates. [...]there did not appear to be any incentive to develop a further model based on degradation data, especially because there was little censored data.

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This article is published as Doganaksoy, N., Hahn, G.J., and Meeker, W.Q., (2013), Providing Better Insights: Improved Life Analyses Using Degradation Testing, Quality Progress, 46, November, 54–56. Posted with permission.

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