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Maintenance Scheduling Using Monitored Parameter Values

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Maintenance, Modeling and Optimization

Abstract

Applications of the reliability methods in maintenance scheduling have been widely investigated in the literature considering failure times. However, information obtained using condition monitoring devices, whenever possible is being used more and more in industries for maintenance scheduling. This trend is accelerated by the availability of reliable sensors and a rapid development in information technologies. The monitored parameter values (MPV) may explain the failure characteristics and influence the maintenance scheduling of a system. There are several reliability models that can be used to model MPV for maintenance scheduling. These models include regression models, proportional hazards family and accelerated failure time family. The latter two appear to be suitable for practical applications.

The paper describes the models that can be used to model the MPV. Some guidelines for selection of suitable models for a given dataset are also discussed. These models are used to estimate the relative importance of the MPV in explaining the failure characteristics. Once the relative importance of the MPV is estimated, either graphical, numerical, or analytical methods can be used for maintenance scheduling based on the MPV. Maintenance cost models that include planned and unplanned maintenance costs are further extended to include the MPV. Graphical methods such as the total time on tests-plot or the cumulative intensity plot can be used to determine the optimum maintenance interval.

The applications of reliability models and graphical methods for determination of the optimum maintenance time interval are illustrated with field failure data. The proposed approach can be used for repairable as well as non-repairable systems.

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References

  1. Aalen, O. O. A Model for Nonparametric Regression Analysis of Counting Processes. Lecture Notes in Statistics, 2 Springer, New York, 1980, 1–25.

    Google Scholar 

  2. Aalen, O. O. A Linear Regression Model for the Analysis of Life Times. Statistics in Medicine, 8 (1989) 907–925.

    Article  Google Scholar 

  3. Ansell, J. I. and Philips, M. J. Practical Problems in the Analysis of Reliability Data. Applied Statistics, 38(2) (1989) 205–247.

    Article  MathSciNet  MATH  Google Scholar 

  4. Ansell, J. I. Practical Reliability Data Analysis. Reliability Engineering and System Safety, 28 (1990) 337–356.

    Article  Google Scholar 

  5. Barlow, R. E. and Proschan, F. Statistical Theory of Reliability and Life Testing. To Begin With, Silver Spring, 1981.

    Google Scholar 

  6. Barlow, R. E. and Campo, R. Total Time on Test Process and Applications to Failure Data Analysis. In Reliability and Fault Tree Analysis, Edited by R. E. Barlow, J. Fussel and N. D. Singpurwalla, SIAM, Philadelphia (1975) 451–481.

    Google Scholar 

  7. Bergman, B. Some Graphical Methods for Maintenance Planning. Annual Reliability and Maintainability Symposium, (1977) 467–471.

    Google Scholar 

  8. Bergman, B. & Klefsj, B. A Graphical Method Applicable to Age Replacement Problems. IEEE Transaction on Reliability, R-31 (1982) 478–481.

    Article  Google Scholar 

  9. Bendell, A. and Walls, L. A. Exploring Reliability Data. Quality and Reliability Engineering International, 1 (1985) 37–51.

    Article  Google Scholar 

  10. Bendell, A., Wightman, D. W. and Walker, E. V. Applying Proportional Hazards Modelling in Reliability. Reliability Engineering and System Safety, 34 (1991) 35–53.

    Article  Google Scholar 

  11. Bennet, S. Log-logistic Regression Model for Survival Data Analysis. Applied Statistics, 32(2) (1983) 165–171.

    Article  Google Scholar 

  12. Bennet, S. Analysis of Survival Data by Proportional Odds Model. Statistics in Medicine, 2 (1983) 273–277.

    Article  Google Scholar 

  13. Breslow, N. Covariance Analysis of Censored Survival Data. Biometrics, 30 (1974) 89–99.

    Article  Google Scholar 

  14. Brown, M. & Proschan, F. Imperfect Repair. Journal of Applied Probability, 20 (1983) 851–859.

    Article  MathSciNet  MATH  Google Scholar 

  15. Cho, D. I. and Parlar, M. A Survey of Maintenance Models for Multi-Unit Systems. European Journal of Operational Research, 51 (1991) 1–23.

    Article  Google Scholar 

  16. Ciampi, A. and Etezadi-Amoli, J. A General Model for Testing the Proportional Hazards and the Accelerated Failure Time Hypothesis in the Analysis of Censored Survival Data with Covariates. Communication in Statistics-Theory and Methods, 14(3) (1985) 651–667.

    Article  Google Scholar 

  17. Cox, D. R. Regression Models and Life-Tables. Journal of Royal Statistical Society, B34 (1972) 187–220.

    Google Scholar 

  18. Cox, D. R. The Statistical Analysis of Dependencies in Point Process. In Symposium on point Processes, Edited by P. A. W. Lewis, 1972, 55–66, New York.

    Google Scholar 

  19. Cox, D. R. Partial Likelihood, Biometrika,62 (1975) 188–190.

    Google Scholar 

  20. Cox, D. R. & Lewis, P. A. W. The Statistical Analysis of Series of Events. Chapman & Hall, England, 1966.

    MATH  Google Scholar 

  21. Cox, D. R. and Oakes, D. Analysis of Survival Data. Chapman and Hall, New York, 1984.

    Google Scholar 

  22. Crowder, M. J., Kimber, A. C., Smith, R. L. and Sweeting, T. J. Statistical Analysis of Reliability Data. Chapman & Hall, New York, 1991.

    Google Scholar 

  23. Dohi, T., Kaio, N. and Osaki, S. Solution Procedure for a repairlimit problem using the TTT concept. IMA Journal of Mathematics Applied in Business and Industry, 6(1) (1995) 101–111.

    MathSciNet  MATH  Google Scholar 

  24. Etezadi-Amoli, J. and Ciampi, A. Extended Hazard Regression for Censored Survival Data with Covariates: A Spline Approximation for the Baseline Hazard Function. Biometrics, 43 (1987) 181–192.

    Article  MATH  Google Scholar 

  25. Guo, R. and Love, C. E. Statistical Analysis of an Age Model for Imperfectly Repaired Systems. Quality and Reliability Engineering International, 8 (1992) 133–146.

    Article  Google Scholar 

  26. Hyland, A. and Rausand, M. System Reliability Theory: Models and Statistical Methods. John Wiley & Sons, New York, 1994.

    Google Scholar 

  27. Jardine, A. K. S. Anderson, P. M. and Mann, D. S. Application of the Weibull Proportional Hazards Model to Aircraft and Marine Engine Failure Data. Quality and Reliability Engineering International 3 (1987) 77–82.

    Article  Google Scholar 

  28. Kaplan, E. L. and Meier, P. Nonparametric Estimation from Incomplete Observations. Journal of American Statistical Association, 53 (1958) 457–481.

    Article  MathSciNet  MATH  Google Scholar 

  29. Kalbfleisch, J. D. and Prentice, R. L. The Statistical Analysis of Failure Time Data. John Wiley & Sons, New York, 1980.

    MATH  Google Scholar 

  30. Klefsj, B. TTT-transforms-A Useful Tool when Analysing Different Reliability Problems. Reliability Engneering, 4 (1986) 231–241.

    Article  Google Scholar 

  31. Kumar, D. Proportional Hazards Modelling of Repairable Systems. Quality and Reliability Engineering International, 11 (1995) 361–369.

    Article  Google Scholar 

  32. Kumar, D. and Klefsj, B. Proportional Hazards Model: A Review. Reliability Engineering and System Safety, 44 (1994) 177–188.

    Article  Google Scholar 

  33. Kumar, D. and Westberg, U. Proportional Hazards Modelling of Time-Dependent Covariates Using a Linear Regression: A Case Study. IEEE Transactions on Reliability, 45(3) (1996) 386–392.

    Article  Google Scholar 

  34. Kumar, D. and Westberg, U. Some Reliability Models for Analysing the Effects of Operating Conditions. International Journal of Reliability, Quality and Safety Engineering, 4(2), (1997) 133–148.

    Article  Google Scholar 

  35. Kumar, D. and Westberg, U. Maintenance scheduling under Age Replacement Policy using Proportional Hazards Model and TTT-plotting. European Journal of Operational Research, 99(3), (1997) 507–515.

    Article  MATH  Google Scholar 

  36. Kumar U, Klefsj, B. and Granholm, S. Reliability Investigation for a Fleet of Load Haul Dump Machines in a Swedish Mine. Reliability Engineering and System Safety, 26 (1989) 341–369.

    Article  Google Scholar 

  37. Lancaster, T. The Econometric Analysis of Transition Data. Cambridge University Press, UK, 1990.

    MATH  Google Scholar 

  38. Lawless, J. F. Statistical Models and Methods for Lifetime Data. John Wiley & Sons, New York, 1982.

    MATH  Google Scholar 

  39. Lawless, J. F. Statistical Methods in Reliability. Technometrics, 25(4) (1983) 305–335.

    Article  MathSciNet  MATH  Google Scholar 

  40. Lawless, J. F. A Note on Lifetime Regression Models. Biometrika, 73(2) (1986) 509–512.

    Article  MathSciNet  MATH  Google Scholar 

  41. Lawless, J. F. Regression Methods for Poisson Process Data. Journal of American Statistical Association, 82 (1987) 805–815.

    Article  MathSciNet  Google Scholar 

  42. Louis, A. T. Nonparametric Analysis of an Accelerated Failure Time Model. Biometrika, 68(2) (1981) 381–390.

    Article  MathSciNet  MATH  Google Scholar 

  43. Love, C. E. and Guo, R. Using Proportional Hazard Modelling in Plant Maintenance. Quality and Reliability Engineering International, 7 (1991) 7–17.

    Article  Google Scholar 

  44. McCullagh, P. Regression Models for Ordinal Data (with discussion). Journal of Royal Statistical Society, B42 (1980) 109-142.

    Google Scholar 

  45. McCullagh, P. and Neder, J. A. Generalized Linear Models Chapman and Hall. London, 1989.

    MATH  Google Scholar 

  46. Nelson, W. Hazard Plotting for Incomplete Failure Data. Journal of Quality Technology, 1 (1969) 27–52.

    Google Scholar 

  47. Nelson, W. Accelerated Testing: Statistical Models, Test Plans, and Data Analyses. Wiley & Sons, New York, 1990.

    Google Scholar 

  48. Newby, M. Accelerated Failure Time Models for Reliability Data Analysis. Reliability Engineering and System Safety, 20 (1988) 187–197.

    Article  Google Scholar 

  49. Newby, M. A Critical look at Some Point Process Models for Repairable Systems. IMA Journal of Mathematics Applied in Business and Industry, 4 (1993) 375–394.

    Google Scholar 

  50. Newby, M. Why no Additive Hazards Model. IEEE Transaction on Reliability, 43(3) (1994) 484–488.

    Article  Google Scholar 

  51. Pijnenburg, M. Additive Hazards Models in Repairable systems Reliability. Reliability Engineering and System Safety, 31 (1991) 369–390.

    Article  Google Scholar 

  52. Pike, M. C. A Suggested Method of Analysis of a Certain Class of Experiments in Carcinogenesis. Biometrics, 22 (1966) 142–161.

    Article  Google Scholar 

  53. Prentice, R. L, Williams, B. J. and Peterson, A. V., On the Regression Analysis of Multivariate Failure Data. Biometrika, 68(2) (1981) 373–379.

    Article  MathSciNet  MATH  Google Scholar 

  54. Ridder, G. The Non-parametric Identification of Generalized Accelerated Failure Time Models. Review of Economic Statistic, 57 (1990) 167–182.

    Article  MathSciNet  MATH  Google Scholar 

  55. Smith, R. L. Weibull regression Models for Reliability Data. Reliability Engineering and System Safety, 34 (1991) 35–57.

    Article  Google Scholar 

  56. Solomon, P. J. Effect of Misconception in the Regression Analysis of Survival Data. Biometrika, 71 (1984) 291–308. Amendment, 73 (1986) 245.

    Article  MathSciNet  MATH  Google Scholar 

  57. Valdez-Flores, C. and Feldman, R. M. A Survey of Preventive Maintenance Models for Stochastically Deteriorating Single-unit System. Naval Research Logistics, 36 (1989) 419–446.

    Article  MathSciNet  MATH  Google Scholar 

  58. Vaupel, J. W., Manton, K. G. and Stallard, E. The Impact of Heterogeneity in Individual Frailty on the Dynamics of Mortality. Demography, 16 (1979) 439–454.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Reliability Engineer Nokia Svenska AB, PO BOx 1070, 164 64, Kista, Sweden

    Dhananjay Kumar

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  1. Dhananjay Kumar
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Editors and Affiliations

  1. Department of Systems Engineering College of Computer Sciences & Engineering, King Fahd University of Petroleum & Minerals, Saudi Arabia

    Mohamed Ben-Daya , Salih O. Duffuaa  & Abdul Raouf ,  & 

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Kumar, D. (2000). Maintenance Scheduling Using Monitored Parameter Values. In: Ben-Daya, M., Duffuaa, S.O., Raouf, A. (eds) Maintenance, Modeling and Optimization. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4329-9_14

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  • DOI: https://doi.org/10.1007/978-1-4615-4329-9_14

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-6944-8

  • Online ISBN: 978-1-4615-4329-9

  • eBook Packages: Springer Book Archive

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