• International Journal of Reliability and Applications
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• v.10 no.2
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• pp.101-107
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• 2009

In the accelerated tests, the importance of correct failure analysis must be strongly emphasized. Understanding the failure mechanisms is requisite for designing and conducting successful accelerated life test. Under this presumption, a rational method must be identified to relate the results of accelerated tests quantitatively to the reliability or failure rates in use conditions, using a scientific acceleration transform. Most widely used models for relating the results of accelerated tests quantitatively to the reliability or failure rates in use conditions are an accelerated failure time model and a proportional hazards model. The purpose of this research is to compare the usability of the accelerated failure time model and proportional hazards model in the accelerated life tests.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.467-478
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• 2016

The accelerated failure time model or accelerated life model relates the logarithm of the failure time linearly to the covariates. The parameters in the model provides a direct interpretation. In this paper, we review some newly developed practically useful estimation and inference methods for the model in the analysis of right censored data.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.268-275
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• 2003

We consider the Bayesian accelerated failure time model. The error distribution is assigned a skewed normal distribution which is including normal distribution. For noninformative priors of regression coefficients, we show the propriety of posterior distribution. A Markov Chain Monte Carlo algorithm(i.e., Gibbs Sampler) is used to obtain a predictive distribution for a future observation and Bayes estimates of regression coefficients.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.765-775
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• 2010

For the accelerated failure time (AFT) model a lot of effort has been devoted to develop effective estimation methods. AFT model assumes a linear relationship between the logarithm of event time and covariates. In this paper we propose a semiparametric support vector machine to consider situations where the functional form of the effect of one or more covariates is unknown. The proposed estimating equation can be computed by a quadratic programming and a linear equation. We study the effect of several covariates on a censored response variable with an unknown probability distribution. We also provide a generalized approximate cross-validation method for choosing the hyper-parameters which affect the performance of the proposed approach. The proposed method is evaluated through simulations using the artificial example.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.208-213
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• 2008

In order to assess the reliability of the electronics control unit for vehicles, accelerated life test model and procedure are developed. By using this method, failure mechanism and life distribution are analyzed. The main results are as follows : i) the main failure mechanism is degradation failure that is, junction destruction of a semiconductor resin by high temperature. ii) the life distribution of the electronics control unit for vehicles is fitted well to Weibull life distribution and the accelerated life model of that is fitted well to Arrhenius model. iii) at the result of the life distribution, accelerated life test method is developed, and test time for life assessment will be shortened by 5,000 hours by this test method.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.445-450
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• 2003

This study was performed 10 develop the accelerated life test method using Weibull-IPL(Inverse Power Law) model for mechanical components. Weibull-IPL model is concerned with determining the assurance life with confidence level and the accelerated life test time From the relation of weibull distribution factors and confidence limit, the testing times on the no number of failure acceptance criteria arc determined. The mechanical components generally represent wear and fatigue characteristics as a failure mode. IPL based on the cumulative damage theory is applied effectively the mechanical components to reduce the testing time and to achieve the accelerating test conditions. As the actual application example, accelerated life test method of agricultural tractor transmission was described. Life distribution of agricultural tractor transmission was supposed to follow Weibull distribution and life test time was calculated under the conditions of average life (MTBF) 3,000 hours and 90% confidence level for one test sample. According to IPL, because test time call be shorten in case increase test load test time could be reduced by 482 hours when we put the load 1.1 times of rated load than 0.73 times of rated load that is equivalent load calculated by load spectrum of the agricultural tractor. This time, acceleration coefficient was 11.7.

• Journal of Applied Reliability
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• v.17 no.2
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• pp.103-111
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• 2017

Purpose: Accelerated degradation tests can speed time to market and reduce the test time and costs associated with long term reliability tests to verify the required service life of a product or material. This paper proposes a service life prediction method for components or materials using an accelerated degradation tests based on the relationships between temperature and the rate of failure-causing chemical reaction. Methods: The relationship between performance degradation and the rate of a failure-causing chemical reaction is assumed and least square estimation is used to estimate model parameters from the degradation model. Results: Methods of obtaining acceleration factors and predicting service life using the degradation model are presented and a numerical example is provided. Conclusion: Service life prediction of a component or material is possible at an early stage of the degradation test by using the proposed method.

• Journal of Applied Reliability
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• v.3 no.2
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• pp.103-116
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• 2003

We carry out reliability tests and investigate the failure mechanisms. of the wafer level vacuum packaged MEMS gyroscope sensor using an accelerated degradation test. The accelerated degradation test (ADT) is used to evaluate reliability (and/or life) of the MEMS vacuum package and to select the accelerated test conditions, which reduce the reliability testing time. Using the failure distribution model and stress-life model, we are able to estimate the average life time of the vacuum package, which is well agreed with the measured data. After improving several package reliability issues such as prevention of gas diffusion through package, we carry out another set of accelerated tests at the chosen acceleration level. The results show that reliability of the vacuum packaged gyroscope has been greatly improved and can survive without degradation of performance, which is the Q-factor in gyroscope sensor, during environmental stress reliability tests.

• Proceedings of the Korean Reliability Society Conference
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• 2005.06a
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• pp.327-337
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• 2005

Accelerated life test models and procedure are developed to assess the reliability of Door switch. The main function of door switch is to operate bulb lamp and fan motor. The accelerated life test method and test equipments are developed using the relationship between stresses and life characteristics of the products. Using the developed accelerated life test method, the parameters of the ALT model and life time distribution are estimated and the reliability of the Door S/W at use condition if assessed. The proposed accelerated life test method and procedure may be extended and applied to testing similar kinds of products to reduce test time and costs of the tests remarkably.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.2
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• pp.145-149
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• 2003

We consider the estimation of failure rate and acceleration factor under type-I censoring without using acceleration model when testing is conducted in only one highly accelerated condition. Failure times of an item are assumed to be exponentially distributed. It is also assumed that the uncertainty about the acceleration factor, the failure time contraction ratio between accelerated condition and use condition, can be modeled by the uniform or gamma prior distribution of appropriate parameters. We respectively use Bayes and maximum likelihood approaches to estimate acceleration factor and failure rate in the use condition. An example is given to show how the method can be applied.