Document Type : Original Article

Author

Department of Mathematics and Engineering Science, Imam Khomeini International University -Buin Zahra Higher Education Center of Engineering and Technology, Buin Zahra, Iran.

Abstract

Purpose: In this paper k- level constant stress accelerated life test under Type-I progressive censoring for Lomax distribution with non-constant shape and scale parameters is investigated. The purpose of this paper is to estimate the model parameters using the EM algorithm and optimize the test design.

Methodology: Life testing often consumes a very long time for testing and this is a fundamental problem in test design. This problem is solved by accelerated life tests. There is a recommended method for reducing the time of failure, such that the stress level of the test units will increase and then they will fail earlier than normal operating conditions. Therefore, these approaches reduced the running time. In this paper, the k-level constant stress accelerated life test under progressive Type-I censoring used. The Expectation-Maximization (EM) algorithm is used to determine the maximum likelihood estimates of the unknown parameters. This algorithm is a very powerful tool in handling the incomplete data problem. Two different criteria used to optimize the test plan. And the effect of the sample size, number of stress levels and inspection and the intermediate censoring proportion are assessed on the design efficiency.

Findings: based on the simulation study and a real data set, it is demonstrated that the EM estimator is good. Also, under the optimization criterion II, a more efficient test was obtained than the optimization criterion I. In addition, the small sample size, the small number of stress levels, the small number of inspections and the large intermediate censoring proportion lead to a more efficient test.

Originality/Value: In this paper, the periodic inspection is used to collect lifetime data. Although continuous is an ideal mode. But sometimes due to technical limitations and/or budgetary constraints, the continuous inspection is not possible in practice and the experimenter has to use the periodic inspection. In this case, the exact times of test units may not be available and only the failure counts are collected at certain time points during the test. Also, in this paper, we assumed that both scale and shape parameters to be log linear model by operating stress

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Main Subjects

Balakrishnan, N., & Han, D. (2008). Exact inference for a simple step-stress model with competing risks for failure from exponential distribution under Type-II censoring. Journal of statistical planning and inference138(12), 4172-4186. https://doi.org/10.1016/j.jspi.2008.03.036
Balakrishnan, N., Kundu, D., Ng, K. T., & Kannan, N. (2007). Point and interval estimation for a simple step-stress model with Type-II censoring. Journal of quality technology39(1), 35-47. https://doi.org/10.1080/00224065.2007.11917671
Boyko, K. C., & Gerlach, D. L. (1989, April). Time dependent dielectric breakdown at 210 Å oxides. 27th annual proceedings. International reliability physics symposium (pp. 1-8). IEEE. DOI: 10.1109/RELPHY.1989.36309
Budhiraja, S., & Pradhan, B. (2020). Point and interval estimation under progressive type-I interval censoring with random removal. Statistical papers61(1), 445-477. https://doi.org/10.1007/s00362-017-0948-y
Chandra, N., & Khan, M. A. (2018). Analysis and optimum plan for 3-step step-stress accelerated life tests with Lomax model under progressive type-I censoring. Communications in mathematics and statistics6(1), 73-90. https://doi.org/10.1007/s40304-017-0123-8
Fan, T. H., & Hsu, T. M. (2014). Constant stress accelerated life test on a multiple-component series system under Weibull lifetime distributions. Communications in statistics-theory and methods43(10-12), 2370-2383. https://doi.org/10.1080/03610926.2013.809115
Guan, Q., Tang, Y., Fu, J., & Xu, A. (2014). Optimal multiple constant-stress accelerated life tests for generalized exponential distribution. Communications in statistics-simulation and computation43(8), 1852-1865. https://doi.org/10.1080/03610918.2013.810257
Hassan, A. S., Assar, S. M., & Shelbaia, A. (2016). Optimum step-stress accelerated life test plan for Lomax distribution with an adaptive type-II progressive hybrid censoring. Journal of advances in mathematics and computer science, 13(2), 1-19. DOI: 10.9734/10.9734/BJMCS/2016/21964
Hiergeist, P., Spitzer, A., & Rohl, S. (1989). Lifetime of oxide and oxide-nitro-oxide dielectrics within trench capacitors for DRAM’s. IEEE transactions on electron devices, 36, 913-919. DOI: 10.1109/16.299673
Kateri, M., & Kamps, U. (2015). Inference in step-stress models based on failure rates. Statistical papers56(3), 639-660. https://doi.org/10.1007/s00362-014-0601-y
Khaje Zadeh, S., Shahverdiani, S., Daneshvar, A., & Madanchi Zaj, M. (2020). Predicting the optimal stock portfolio approach of meta-heuristic algorithm and Markov decision process. Journal of decisions and operations research5(4), 426-445. (In Persian). DOI: 10.22105/dmor.2020.239616.1183
Khoshfetrat, S., & Hosseinzadeh Lotfi, F. (2014). Introducing a nonlinear programming model and using genetic algorithm to rank the alternatives in analytic hierarchy process. Journal of applied research on industrial engineering1(1), 12-18.
Li, P. C., Ting, W., & Kwong, D. L. (1989). Time-dependent dielectric breakdown of chemical-vapour-deposited SiO/sub 2/gate dielectrics. Electronics letters25(10), 665-666. DOI:10.1049/EL:19890450
 Little, R., & Rubin, D. (2007). Incomplete data. In S. Kotz & N.L. Johnson (Eds.), Encyclopedia of statistical sciences (pp 46–53). Wiley, New York.   
Lomax, K. S. (1954). Business failures: another example of the analysis of failure data. Journal of the American statistical association49(268), 847-852.
Meeker, W. Q., & Escobar, L. A. (1998). Statistical methods for reliability data. JohnWiley and Sons. Inc., New York.
Mitra, S., Ganguly, A., Samanta, D., & Kundu, D. (2013). On the simple step-stress model for two-parameter exponential distribution. Statistical methodology15, 95-114. https://doi.org/10.1016/j.stamet.2013.04.003
Nelson, W. (1990). Accelerated testing - statistical models, test plans, and data analyses. John Wiley and Sons, New York. DOI:10.2307/2347642
Nelson, W. (1984). Fitting of fatigue curves with nonconstant standard deviation to data with runouts. Journal of testing and evaluation12(2), 69-77. https://doi.org/10.1520/JTE10700J
Parchami, A., DoostMohammadi, M., & Mashinchi, M. (2019). Application of newton raphson algorithm as a numerical method in maximum likelihood estimation. Decisions and operations research3(4), 402-412. (In Persian). DOI: 10.22105/dmor.2019.77084
Parnianifard, A., Ahmad, S. A., Ariffin, M. K. A., & Ismail, M. I. S. (2018). Design and analysis of computer experiments using polynomial regression and Latin hypercube sampling in optimal design of PID controller. Journal of applied research on industrial engineering5(2), 156-168. DOI: 10.22105/jarie.2018.141898.1051
 Pascual, F. (2008). Accelerated life test planning with independent Weibull competing risks. IEEE transactions on reliability57(3), 435-444. DOI: 10.1109/TR.2008.928205
Rashidinejad, A. (2010). Comparison of EM algorithm imputation with two methods of mean imputation and new samples imputation in panel surveys. Ijoss Iranian journal of official statistics studies21(1), 89-108. (In Persian). http://ijoss.srtc.ac.ir/article-1-74-en.html
Wang, L. (2017). Inference of constant-stress accelerated life test for a truncated distribution under progressive censoring. Applied mathematical modelling44, 743-757. https://doi.org/10.1016/j.apm.2017.02.011
Wang, L. (2018). Estimation of constant-stress accelerated life test for Weibull distribution with nonconstant shape parameter. Journal of computational and applied mathematics343, 539-555. https://doi.org/10.1016/j.cam.2018.05.012