Reliability inference for field conditions from accelerated degradation testing

Liao, Haitao; Elsayed, Elsayed A.

doi:10.1002/nav.20163

Cited by 156 publications

(79 citation statements)

References 26 publications

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“…Each mode requires different amounts of current flow, so operating stress varies stochastically. Liao & Elsayed [16] warned that ignoring such stress variations would lead to significant flaws in correctly evaluating field reliability. The further study can include the extension to stochastic operation stresses for the ADT data.…”

Section: Discussion and Future Researchesmentioning

confidence: 99%

“…For example, in the inverse power relationship under the SAFT model (7), is defined as (15) For an estimator of with respect to and , , the number of different estimators of is . These estimators are combined to obtain a pooled estimator of [33]: (16) The cumulative distribution at use stress level is then computed by (17) where for .…”

Section: A Delta Approximation Of Lifetime Distributionmentioning

confidence: 99%

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Direct Prediction Methods on Lifetime Distribution of Organic Light-Emitting Diodes From Accelerated Degradation Tests

2010

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Abstract-Accelerated degradation testing (ADT) expedites product degradation by stressing the product beyond its normal use. To extrapolate the product's reliability at use condition, the ADT requires a known functional link relating the harsh testing environment to the usual use environment. Practitioners are often faced with a great challenge to designate an explicit form of the stress-degradation relationship a priori in accelerated degradation models. In this paper, we propose three methods to make direct inference on the lifetime distribution itself without invoking arbitrary assumptions on the degradation model: delta approximation, multiple imputation of failure-times, and the lifetime distribution-based (LDB) method. The methods are easy to implement without computational difficulty, hence they have potential in a wide range of applications for estimating lifetime distributions from ADT data. We applied the methods to two ADT data sets including a real application of commercial organic light-emitting diodes (OLED). The analysis of the examples and simulation results suggests parametric LDB and multiple imputation method as more potential alternatives to traditional failure-time approaches, especially for the case where there is neither enough physical background, nor historical evidence supporting presumed relationships between stress and the parameters of the degradation model.

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Section: Discussion and Future Researchesmentioning

confidence: 99%

Section: A Delta Approximation Of Lifetime Distributionmentioning

confidence: 99%

Direct Prediction Methods on Lifetime Distribution of Organic Light-Emitting Diodes From Accelerated Degradation Tests

2010

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“…Modeling stress variables into stochastic degradation models were recently discussed by e.g. Padgett and Tomlinson 12 , Park and Padgett 13 , Liao and Elsayed 14 .…”

Section: Literature Review Of Degradation Data Analysismentioning

confidence: 99%

A hierarchical modeling approach to accelerated degradation testing data analysis: A case study

Pan

Crispin

2011

Quality & Reliability Eng

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In this case study, we investigate the degradation process of light-emitting diodes (LEDs), which is used as a light source in DNA sequencing machines. Accelerated degradation tests are applied by varying temperature and forward current, and the light outputs are measured by a computerized measuring system. A degradation path model, which connects to the LED function recommended in Mitsuo (1991), is used in describing the degradation process. We consider variations in both measurement errors and degradation paths among individual test units. It is demonstrated that the hierarchical modeling approach is flexible and powerful in modeling a complex degradation process with nonlinear function and random coefficient. After fitting the model by maximum likelihood estimation, the failure time distribution can be obtained by simulation.

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“…These data could be useful for further applications (reliability analysis or parameter statistical characterisation) if they can be translated to normal conditions. Several studies have been devoted to calibrate the accelerated tests and their data (Volf & Timková, 2014;Wang, Pan, Li, & Jiang, 2013) and to use this information for lifetime or reliability assessment (Haghighi, 2014;Liao & Elsayed, 2006). In specific applications on RC degradation processes, Neves, Branco, & De Brito (2013) established the relationship between accelerated and natural carbonation coefficients using a regression method.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian network framework for statistical characterisation of model parameters from accelerated tests: application to chloride ingress into concrete

Tran

Bastidas‐Arteaga

Schoefs

et al. 2017

Structure and Infrastructure Engineering

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This paper addresses the topic of long-term characterisation and probabilistic modelling of chloride ingress into reinforced concrete (RC) structures. Since the corrosion initiation stage may cover various decades, normal tests which simulate chloride penetration into concrete in laboratory conditions as the same as natural conditions, will require significant experimental times. Hence, long-term lifetime assessment of RC structures under chloride attack remains still a challenge. In practice this problem is solved through the use of accelerated tests which speed up the chloride ingress rate and provide valuable mid-and long-term information on the chloride penetration process. Nevertheless, this information cannot be directly used for parameter statistical characterisation if the equivalent times required in natural conditions to reach the same chloride concentrations in the accelerated tests are unknown. Consequently, this study proposes a novel iterative approach based on Bayesian network updating to estimate chloride ingress model parameters from the data obtained under accelerated laboratory conditions. The Bayesian Network structure and iterative approach are first tested with numerical evidences. Thereafter, the complete proposed methodology is verified with results from real experimental measurements. The results indicate that combining data from normal and accelerated tests significantly reduces the statistical characterisation error of model parameters.

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Reliability inference for field conditions from accelerated degradation testing

Cited by 156 publications

References 26 publications

Direct Prediction Methods on Lifetime Distribution of Organic Light-Emitting Diodes From Accelerated Degradation Tests

Direct Prediction Methods on Lifetime Distribution of Organic Light-Emitting Diodes From Accelerated Degradation Tests

A hierarchical modeling approach to accelerated degradation testing data analysis: A case study

A Bayesian network framework for statistical characterisation of model parameters from accelerated tests: application to chloride ingress into concrete

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