PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Burn-in procedures in accelerated environment and system maintenance policies

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Burn-in is a widely used engineering method which is adopted to eliminate defective items before they are shipped to customers or put into field operation. In order to shorten the burn-in process, burn-in is most often accomplished in an accelerated environment. However, there have been few probabilistic or stochastic models for the burn-in procedures in accelerated environment. In this paper, under a new stochastic model for accelerated burn-in procedure, the problems of determining both optimal accelerated burn-in time and optimal replacement policy are considered. Components are burned-in under an accelerated environment, then those surviving the burn-in procedure are put into field operation and they are maintained under a replacement policy. The properties of the optimal accelerated burn-in time and optimal replacement policy are obtained and a numerical example which illustrates the usage of obtained results will be presented.
Rocznik
Tom
Strony
101--107
Opis fizyczny
Bibliogr. 17 poz.
Twórcy
autor
  • Pukyong National University, Busan, Korea
Bibliografia
  • [1] Block, H. W., Mi, J. & Savits, T. H. (1994). Some results on burn-in, Statistica Sinica 4, 525-533.
  • [2] Block, H. W. & Savits, T. H. (1997). Burn-in, Statistical Science 12, 1-19.
  • [3] Block, H. W., Savits, T. H. & Singh, H. (2002). A criterion for burn-in that balances mean residual life and residual variance, Operations Research 50, 90-296.
  • [4] Cha, J. H. (2000). On a better burn-in procedure, Journal of Applied Probability 37, 1099-1103.
  • [5] Cha, J. H. (2005). A stochastic model for burn-in procedures in accelerated environment, Naval Research Logistics 53, 226-234.
  • [6] Clarotti, C. A. & Spizzichino, F. (1991). Bayes burn-in decision procedures, Probability in the Engineering and Informational Sciences 4, 437-445.
  • [7] Jensen, F. & Petersen, N. E. (1982). Burn-In. New York, Wiley.
  • [8] Kuo, W. (1984). Reliability enhancement through optimal burn-in decision. IEEE Transactions on Reliability 33, 145-156.
  • [9] Kuo, W. & Kuo, Y. (1983). Facing the headaches of early failures: A state-of-the-art review of burn-in decisions, Proc. IEEE. 71, 1257-1266.
  • [10] Meeker, W. Q. & Escobar, L. A. (1993). A review of recent research and current issues of accelerated testing, International Statistical Review 61, 147-168.
  • [11] Mi, J. (1991). Optimal Burn-In. Doctoral Thesis, Dept. Statistics, Univ. Pittsburgh.
  • [12] Mi, J. (1994a). Burn-in and maintenance policies, Advances in Applied Probability 26, 207-221.
  • [13] Mi, J. (1994b). Maximization of a survival probability and its applications, Journal of Applied Probability 31, 1026-1033.
  • [14] Mi, J. (1996). Minimizing some cost functions related to both burn-in and field use, Operations Research 44, 497-500.
  • [15] Nelson, W. (1990). Accelerated testing : Statistical Models, Test Plans, and Data Analysis, New York, John Wiley & Sons. Inc.
  • [16] Nguyen, D. G. & Murthy, D. N. P. (1982). Optimal burn-in time to minimize cost for products sold under warranty, IIE Transactions 14, 167-174.
  • [17] Usami, K. and Yoshioka, A. (2004). Dynamic sleep control for finite-state-machines to reduce active leakage power, IEICE Trans. Fundamentals E87-A, 3116-3123.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9021c4f4-276c-492c-9bcf-40eb3ec910c6
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.