Accelerated Destructive Degradation Tests: Data, Models, and Analysis

Escobar, Luis A.; Meeker, William Q.; Kugler, Danny L.; Kramer, Laura L.

doi:10.1142/9789812795250_0021

Cited by 44 publications

(53 citation statements)

References 9 publications

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“…The plot on the right-hand side of Figure 11shows the transformed data (using log by square root axes to show the data in the untransformed units) along with the diffuse-prior analysis Bayesian estimates of mean log strength µ as a function of time and temperature. These estimates are close to the ML estimates given in Meeker, Kugler, and Kramer (2003).…”

Section: Example 3 Analysis Of the Adhesive Bond B Accelerated Destrusupporting

confidence: 85%

“…The development in this example follows that of Escobar et al (2003), except that we use Bayesian estimation instead of ML. Figure 11 shows the measured strength data for the different combinations of Weeks and Degrees C. On the left the data are plotted on linear axes and on the right the same data are plotted on transformed axes using a log transformation for strength (newtons) and a square root transformation for Weeks (along with a fitted model described below).…”

Section: Example 3 Analysis Of the Adhesive Bond B Accelerated Destrumentioning

confidence: 99%

“…As shown in Meeker, Kugler, and Kramer (2003), the degradation model in ( Figure 12 shows estimates of the fraction failing as a function of time for different temperature levels. The plot on the left is based on the diffuse-prior analysis and is almost exactly the same as the ML analysis given in Escobar, Meeker, Kugler, and Kramer (2003). The estimate for the nominal use conditions of 25 degrees C is accompanied by a set of pointwise 95% credible intervals.…”

Section: Example 3 Analysis Of the Adhesive Bond B Accelerated Destrumentioning

confidence: 99%

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Application of Bayesian Methods in Reliability Data Analyses

Meeker

2014

Journal of Quality Technology

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The development of the theory and application of Monte Carlo Markov Chain methods, vast improvements in computational capabilities and emerging software alternatives have made it possible for more frequent use of Bayesian methods in reliability applications. Bayesian methods, however, remain controversial in Reliability (and some other applications) because of the concern about where the needed prior distributions should come from. On the other hand, there are many applications where engineers have solid prior information on certain aspects of their reliability problems based on physics of failure or previous experience with the same failure mechanism. For example, engineers often have useful but imprecise knowledge about the effective activation energy in a temperature-accelerated life test or about the Weibull shape parameter in the analysis of fatigue failure data. In such applications, the use of Bayesian methods is compelling as it offers an appropriate compromise between assuming that such quantities are known and assuming that nothing is known. In this paper we compare the use of Bayesian methods with the traditional maximum likelihood methods for a group of examples including the analysis of field data with multiple censoring, accelerated life test data, and accelerated degradation test data. The development of the theory and application of Monte Carlo Markov Chain methods, vast improvements in computational capabilities and emerging software alternatives have made it possible for more frequent use of Bayesian methods in reliability applications. Bayesian methods, however, remain controversial in Reliability (and some other applications) because of the concern about where the needed prior distributions should come from. On the other hand, there are many applications where engineers have solid prior information on certain aspects of their reliability problems based on physics of failure or previous experience with the same failure mechanism. For example, engineers often have useful but imprecise knowledge about the effective activation energy in a temperature-accelerated life test or about the Weibull shape parameter in the analysis of fatigue failure data. In such applications, the use of Bayesian methods is compelling as it offers an appropriate compromise between assuming that such quantities are known and assuming that nothing is known. In this paper we compare the use of Bayesian methods with the traditional maximum likelihood methods for a group of examples including the analysis of field data with multiple censoring, accelerated life test data, and accelerated degradation test data.

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Section: Example 3 Analysis Of the Adhesive Bond B Accelerated Destrusupporting

confidence: 85%

Section: Example 3 Analysis Of the Adhesive Bond B Accelerated Destrumentioning

confidence: 99%

Section: Example 3 Analysis Of the Adhesive Bond B Accelerated Destrumentioning

confidence: 99%

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Application of Bayesian Methods in Reliability Data Analyses

Meeker

2014

Journal of Quality Technology

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“…Meeker, Kugler and Kramer [5]). It would be interesting to develop methods parallel to ours where the degradation measure follows a log-location-scale distribution.…”

Section: Discussionmentioning

confidence: 99%

A Tool for Evaluating Time-Varying-Stress Accelerated Life Test Plans With Log-Location-Scale Distributions

Hong

Ma²,

Meeker

2010

IEEE Trans. Rel.

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Accelerated life tests (ALTs) are often used to make timely assessments of the life time distribution of materials and components. The goal of many ALTs is estimation of a quantile of a log-location failure time distribution. Much of the previous work on planning accelerated life tests has focused on deriving testplanning methods under a specific log-location distribution. This paper presents a new approach for computing approximate large-sample variances of maximum likelihood estimators of a quantile of general loglocation distribution with censoring and time-varying stress. The approach is based on a cumulative exposure model. Using sample data from a published paper describing optimum ramp-stress test plans, we show that our approach and the one used in the previous work give the same variance-covariance matrix of the quantile estimator from the two different approaches. Then, as an application of this approach, we extend the previous work to a new optimum ramp-stress test plan obtained by simultaneously adjusting the ramp rate and the lower start level of stress. We find that the new optimum test plan can have a smaller variance than that of the optimum ramp-stress test plan previously obtained by adjusting only the ramp rate. We also compare optimum ramp-stress test plans with the more commonly used constant-stress accelerated life test plans. We also conduct simulations to provide insight and to check the adequacy of the large-sample approximate results obtained by the approach. This paper presents a new approach for computing approximate large-sample variances of maximum likelihood estimators of a quantile of general a log-location-scale distribution with censoring and time-varying stress. The approach is based on a cumulative exposure model. Using sample data from a published paper describing optimum ramp-stress test plans, we show that our approach and the one used in the previous work give the same variance-covariance matrix of the quantile estimator from the two different approaches. Then, as an application of this approach, we extend the previous work to a new optimum ramp-stress test plan obtained by simultaneously adjusting the ramp rate and the lower start level of stress. We find that the new optimum test plan can have a smaller variance than that of the optimum ramp-stress test plan previously obtained by adjusting only the ramp rate. We compare optimum ramp-stress test plans with the more commonly used constant-stress accelerated life test plans. We also conduct simulations to provide insight and to check the adequacy of the large-sample approximate results obtained by the approach. Index TermsCumulative exposure model; Large-sample approximate variance; Lognormal, Maximum likelihood, Ramp-stress, Step-stress, Weibull. s w(t)Cumulative exposure at time t.

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“…For those products in the design phase or with the limits of test funds, the sample size is small. And it will be smaller when coping with destructive measurement [3]. Small sample lead to the appearance of disordered data and traditional method is not functioning well.…”

Section: Introductionmentioning

confidence: 99%

The Evaluation of Storage Life when Disordered Data Appear in Accelerated Performance Degradation Tests

Li¹

2015

Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering

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Abstract-Motivated by the fact that disordered data may appear in accelerated performance degradation tests when the sample is small, a new storage life evaluation method is proposed to mining information from the existing data. Combining the knowledge that the mean value of the performance parameter will degrade, isotonic regression are used to order the data. Meanwhile, the variance of the parameter is revised by mechanism consistency boundary. Finally, with the ordered data, linear degradation model and accelerated model are used to evaluate the storage life. The prediction method is applied to a battery's data which contains disordered data from accelerated performance degradation tests as example and works well.

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Accelerated Destructive Degradation Tests: Data, Models, and Analysis

Cited by 44 publications

References 9 publications

Application of Bayesian Methods in Reliability Data Analyses

Application of Bayesian Methods in Reliability Data Analyses

A Tool for Evaluating Time-Varying-Stress Accelerated Life Test Plans With Log-Location-Scale Distributions

The Evaluation of Storage Life when Disordered Data Appear in Accelerated Performance Degradation Tests

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