380
Views
17
CrossRef citations to date
0
Altmetric
Original Articles

An Ameliorated Improvement Factor Model for Imperfect Maintenance and Its Goodness of Fit

&
Pages 237-246 | Received 01 Apr 2015, Published online: 12 Apr 2017

References

  • Ahmad, R., and Kamaruddin, S. (2012), “An Overview of Time-Based and Condition-Based Maintenance in Industrial Application,” Computers & Industrial Engineering, 63, 135–149.
  • Anatolyev, S., and Kosenok, G. (2005), “An Alternative to Maximum Likelihood Based on Spacings,” Econometric Theory, 21, 472–476.
  • Block, H. W., Borges, W. S., and Savits, T. H. (1985), “Age-Dependent Minimal Repair,” Journal of Applied Probability, 22, 370–385.
  • Cheng, R. C. H., and Amin, N. A. K. (1983), “Estimating Parameters in Continuous Univariate Distributions With a Shifted Origin,” Journal of the Royal Statistical Society, Series B, 45, 394–403.
  • Darling, D. A. (1955), “The Cramer-Smirnov Test in the Parametric Case,” The Annals of Mathematical Statistics, 26, 1–20.
  • David, F. N., and Johnson, N. L. (1948), “The Probability Integral Transformation When Parameters are Estimated From the Sample,” Biometrika, 35, 182–190.
  • de Toledo, M. L. G., Freitas, M. A., Colosimo, E. A., and Gilardoni, G. L. (2015), “ARA and ARI Imperfect Repair Models: Estimation, Goodness-of-Fit and Reliability Prediction,” Reliability Engineering & System Safety, 140, 107–115.
  • Dijoux, Y., and Idee, E. (2013), “Classes of Virtual Age Models Adapted to Systems With a Burn-In Period,” IEEE Transactions on Reliability, 62, 754–763.
  • Durbin, J. (1973), “Weak Convergence of the Sample Distribution Function When Parameters are Estimated,” The Annals of Statistics, 1, 279–290.
  • Ekstrom, M. (1998), “On the Consistency of the Maximum Spacing Method,” Journal of Statistical Planning and Inference, 70, 209–224.
  • ——— (2001), “Consistency of Generalized Maximum Spacing Estimates,” Scandinavian Journal of Statistics, 28, 343–354.
  • Gentle, J. E. (2003), Random Number Generation and Monte Carlo Methods (2nd ed.), New York: Springer.
  • Guo, C., Wang, W., Guo, B., and Si, X. (2013), “A Maintenance Optimization Model for Mission-Oriented Systems Based on Wiener Degradation,” Reliability Engineering & System Safety, 111, 183–194.
  • Khatab, A., Ait-Kadi, D., and Rezg, N. (2014), “Availability Optimisation for Stochastic Degrading Systems Under Imperfect Preventive Maintenance,” International Journal of Production Research, 52, 4132–4141.
  • Khojandi, A., Maillart, L. M., and Prokopyev, O. A. (2014), “Optimal Planning of Life-Depleting Maintenance Activities,” IIE Transactions, 46, 636–652.
  • Kijima, M. (1989), “Some Results for Repairable Systems With General Repair,” Journal of Applied Probability, 26.
  • Li, L., Hanson, T., Damien, P., and Popova, E. (2014), “A Bayesian Nonparametric Test for Minimal Repair,” Technometrics, 56, 393–406.
  • Lin, D., Zuo, M. J., and Yam, R. C. M. (2000), “General Sequential Imperfect Preventive Maintenance Models,” International Journal of Reliability, Quality and Safety Engineering, 7, 253–266.
  • Lindqvist, B. H. (2006), “On the Statistical Modeling and Analysis of Repairable Systems,” Statistical Science, 21, 532–551.
  • Liu, Y., Huang, H., Wang, Z., Li, Y., and Yang, Y. (2013), “A Joint Redundancy and Imperfect Maintenance Strategy Optimization for Multi-State Systems,” IEEE Transactions on Reliability, 62, 368–378.
  • Liu, Y., Huang, H., and Zhang, X. (2012), “A Data-Driven Approach to Selecting Imperfect Maintenance Models,” IEEE Transactions on Reliability, 61, 101–112.
  • Malik, M. (1979), “Reliable Preventive Maintenance Policy,” AIIE Transactions, 11, 221–228.
  • Melchor-Hernandez, C. L., Rivas-Davalos, F., Maximov, S., Coria, V., and Guardado, J. (2015), “A Model for Optimizing Maintenance Policy for Power Equipment,” International Journal of Electrical Power & Energy Systems, 68, 304–312.
  • Mercier, S., and Castro, I. T. (2013), “On the Modelling of Imperfect Repairs for a Continuously Monitored Gamma Wear Process Through Age Reduction,” Journal of Applied Probability, 50, 1057–1076.
  • Morokoff, W. J., and Caflisch, R. E. (1995), “Quasi-Monte Carlo Integration,” Journal of Computational Physics, 122, 218–230.
  • Nakagawa, T. (1979), “Optimum Policies When Preventive Maintenance is Imperfect,” IEEE Transactions on Reliability, R-28, 331–332.
  • Niederreiter, H. (1978), “Quasi-Monte Carlo Methods and Pseudo-Random Numbers,” Bulletin of the American Mathematical Society, 84, 957–1041.
  • Pandey, M., Zuo, M. J., and Moghaddass, R. (2013), “Selective Maintenance Modeling for a Multistate System With Multistate Components Under Imperfect Maintenance,” IIE Transactions, 45, 1221–1234.
  • Papageorgiou, A. (2001), “Fast Convergence of Quasi-Monte Carlo for a Class of Isotropic Integrals,” Mathematics of Computation, 70, 297–306.
  • Park, J., Chang, W., and Lie, C. (2012), “Stress-Reducing Preventive Maintenance Model for a Unit Under Stressful Environment,” Reliability Engineering & System Safety, 108, 42–48.
  • Pulcini, G. (2013), “A Model-Driven Approach for the Failure Data Analysis of Multiple Repairable Systems Without Information on Individual Sequences,” IEEE Transactions on Reliability, 62, 700–713.
  • Ramirez, P. A. P., and Utne, I. B. (2013), “Decision Support for Life Extension of Technical Systems Through Virtual Age Modelling,” Reliability Engineering & System Safety, 115, 55–69.
  • Ranneby, B. (1984), “The Maximum Spacing Method-An Estimation Method Related to the Maximum Likelihood Method,” Scandinavian Journal of Statistics, 11, 93–112.
  • Schlier, C. (2004), “Error Trends in Quasi-Monte Carlo Integration,” Computer Physics Communications, 159, 93–105.
  • Shafiee, M., and Chukova, S. (2013), “Maintenance Models in Warranty: A Literature Review,” European Journal of Operational Research, 229, 561–572.
  • Shafiee, M., Chukova, S., and Finkelstein, M. (2012), “Warranty and Optimal Upgrade Strategy for Used Systems: An Electric Drill Case Study,” Asia-Pacific Journal of Operational Research, 29, article ID 1250023.
  • Shorack, G., and Wellner, J. (2009), Empirical Processes With Applications to Statistics, Classics in Applied Mathematics, Philadelphia, PA: Society for Industrial and Applied Mathematics.
  • Singpurwalla, N. D. (2006), “The Hazard Potential,” Journal of the American Statistical Association, 101, 1705–1717.
  • Sloan, I. H., and Wozniakowski, H. (1998), “When Are Quasi-Monte Carlo Algorithms Efficient for High Dimensional Integrals?,” Journal of Complexity, 14, 1–33.
  • Stute, W., Manteiga, W., and Quindimil, M. (1993), “Bootstrap Based Goodness-of-Fit-Tests,” Metrika, 40, 243–256.
  • Sukhatme, S. (1972), “Fredholm Determinant of a Positive Definite Kernel of a Special Type and Its Application,” The Annals of Mathematical Statistics, 43, 1914–1926.
  • Swihart, B. J., Goldsmith, J., and Crainiceanu, C. M. (2014), “Restricted Likelihood Ratio Tests for Functional Effects in the Functional Linear Model,” Technometrics, 56, 483–493.
  • Tambe, P., Mohite, S., and Kulkarni, M. (2013), “Optimisation of Opportunistic Maintenance of a Multi-Component System Considering the Effect of Failures on Quality and Production Schedule: A Case Study,” The International Journal of Advanced Manufacturing Technology, 69, 1743–1756.
  • Tanwar, M., Rai, R. N., and Bolia, N. (2014), “Imperfect Repair Modeling Using Kijima Type Generalized Renewal Process,” Reliability Engineering & System Safety, 124, 24–31.
  • Wang, W. (2012), “An Overview of the Recent Advances in Delay-Time-Based Maintenance Modelling,” Reliability Engineering & System Safety, 106, 165–178.
  • Xia, T., Xi, L., Zhou, X., and Du, S. (2012), “Modeling and Optimizing Maintenance Schedule for Energy Systems Subject to Degradation,” Computers & Industrial Engineering, 63, 607–614.
  • Zhang, M., Gaudoin, O., and Xie, M. (2015), “Degradation-Based Maintenance Decision Using Stochastic Filtering for Systems Under Imperfect Maintenance,” European Journal of Operational Research.
  • Zhang, M., Xie, M., and Gaudoin, O. (2013), “A Bivariate Maintenance Policy for Multi-State Repairable Systems With Monotone Process,” IEEE Transactions on Reliability, 62, 876–886.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.