Research Article
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Year 2023, Volume: 7 Issue: 2, 103 - 108, 15.08.2023
https://doi.org/10.35860/iarej.1184070

Abstract

References

  • 1. Cha, J.H. and M. Finkelstein, Point Processes for Reliability Analysis: Shocks and Repairable Systems, in Point Processes for Reliability Analysis: Shocks and Repairable Systems. 2018, Springer: New York. p. 1-419.
  • 2. Lawless, J., Statistical methods in reliability. Technometrics, 1983. 25(4): p. 305-316.
  • 3. Nelson, W., Graphical analysis of system repair data. Journal of Quality Technology, 1988. 20(1): p. 24-35.
  • 4. Trindade, D. and S. Nathan. Simple plots for monitoring the field reliability of repairable systems. in Annual Reliability and Maintainability Symposium, 2005. Proceedings. 2005. IEEE.
  • 5. Block, J., et al., Fleet-level reliability analysis of repairable units: a non-parametric approach using the mean cumulative function. International Journal of Pedagogy, Innovation and New Technologies, 2013. 9(3): p. 333-344.
  • 6. Trindade, D. and S. Nathan, Analysis of repairable systems with severe left censoring or truncation. Quality Engineering, 2018. 30(2): p. 329-338.
  • 7. Rausand, M. and A. Høyland, System reliability theory: models, statistical methods, and applications. Vol. 396. 2003: John Wiley & Sons.
  • 8. Cook, R.J. and J.F. Lawless, The statistical analysis of recurrent events. 2007: Springer.
  • 9. Ascher, H. and H. Feingold, Repairable systems reliability: modeling, inference, misconceptions and their causes. 1984: M. Dekker New York.
  • 10. Garmabaki, A., et al., A reliability decision framework for multiple repairable units. Reliability Engineering & System Safety, 2016. 150: p. 78-88.
  • 11. Kvaløy, J.T. and B.H. Lindqvist, TTT-based tests for trend in repairable systems data. Reliability Engineering & System Safety, 1998. 60(1): p. 13-28.
  • 12. Rigdon, S.E. and A.P. Basu, Statistical methods for the reliability of repairable systems. 2000: Wiley New York.
  • 13. Kvaløy, J.T. and B.H. Lindqvist, A class of tests for renewal process versus monotonic and nonmonotonic trend in repairable systems data, in Mathematical and Statistical Methods in Reliability. 2003, World Scientific. p. 401-414.
  • 14. Viertävä, J. and J.K. Vaurio, Testing statistical significance of trends in learning, ageing and safety indicators. Reliability Engineering & System Safety, 2009. 94(6): p. 1128-1132.
  • 15. Shen, L., B. Cassottana, and L.C. Tang, Statistical trend tests for resilience of power systems. Reliability Engineering & System Safety, 2018. 177: p. 138-147.
  • 16. Kvaløy, J.T. and B.H. Lindqvist, A class of tests for trend in time censored recurrent event data. Technometrics, 2020. 62(1): p. 101-115.
  • 17. Ascher, H.E. and C.K. Hansen, Spurious exponentiality observed when incorrectly fitting a distribution to nonstationary data. IEEE transactions on reliability, 1998. 47(4): p. 451-459.
  • 18. Ascher, H.E., A set-of-numbers is NOT a data-set. IEEE Transactions on Reliability, 1999. 48(2): p. 135-140.
  • 19. Nelson, W.B., Recurrent events data analysis for product repairs, disease recurrences, and other applications. Vol. 10. 2003: SIAM.
  • 20. Zuo, J., W.Q. Meeker, and H. Wu, A simulation study on the confidence interval procedures of some mean cumulative function estimators. Journal of Statistical Computation and Simulation, 2013. 83(10): p. 1868-1889.
  • 21. Chan, K.C.G. and M.-C. Wang, Semiparametric modeling and estimation of the terminal behavior of recurrent marker processes before failure events. Journal of the American Statistical Association, 2017. 112(517): p. 351-362.
  • 22. Nelson, W., Confidence limits for recurrence data—applied to cost or number of product repairs. Technometrics, 1995. 37(2): p. 147-157.
  • 23. Nelson, W.B., Repair Data, Sets of: How to Graph, Analyze, and Compare. Encyclopedia of Statistics in Quality and Reliability, 2008.
  • 24. Zuo, J., W.Q. Meeker, and H. Wu, Analysis of window-observation recurrence data. Technometrics, 2008. 50(2): p. 128-143.
  • 25. Jiang, R., et al., A robust mean cumulative function estimator and its application to overhaul time optimization for a fleet of heterogeneous repairable systems. Reliability Engineering & System Safety, 2023: p. 109265.
  • 26. Nelson, W.B., Repair Data, Sets of: How to Graph, Analyze, and Compare. Encyclopedia of Statistics in Quality and Reliability, 2008. 4.
  • 27. Nelson, W.B., Recurrent events data analysis for product repairs, disease recurrences, and other applications. 2003: SIAM.
  • 28. William, W. and L.A. Escobar, Statistical methods for reliability data. A. Wiley Interscience Publications, 1998.

Non-parametric analysis of maintenance data for Attitude Indicator of a commercial aircraft fleet

Year 2023, Volume: 7 Issue: 2, 103 - 108, 15.08.2023
https://doi.org/10.35860/iarej.1184070

Abstract

Analysis of maintenance data for a repairable system provides information about the failure behavior of the system. Such information is needed for determining preventive maintenance and retirement policy for the system. Parametric and non-parametric models can be used for analysis. Parametric models require more assumptions about the failure process of the systems under consideration compared to non-parametric models. To verify these assumptions statistical expertise needed. The purpose of this paper is to show that in practice non-parametric estimator of mean cumulative function can be utilized easily to model the failure behavior of a fleet. Mean cumulative function estimates the mean number of failures as function of operating hours. The method is exemplified on the attitude indicator units of a commercial aircraft fleet. Sampling uncertainty of the estimates is quantified by normal approximation confidence intervals.

References

  • 1. Cha, J.H. and M. Finkelstein, Point Processes for Reliability Analysis: Shocks and Repairable Systems, in Point Processes for Reliability Analysis: Shocks and Repairable Systems. 2018, Springer: New York. p. 1-419.
  • 2. Lawless, J., Statistical methods in reliability. Technometrics, 1983. 25(4): p. 305-316.
  • 3. Nelson, W., Graphical analysis of system repair data. Journal of Quality Technology, 1988. 20(1): p. 24-35.
  • 4. Trindade, D. and S. Nathan. Simple plots for monitoring the field reliability of repairable systems. in Annual Reliability and Maintainability Symposium, 2005. Proceedings. 2005. IEEE.
  • 5. Block, J., et al., Fleet-level reliability analysis of repairable units: a non-parametric approach using the mean cumulative function. International Journal of Pedagogy, Innovation and New Technologies, 2013. 9(3): p. 333-344.
  • 6. Trindade, D. and S. Nathan, Analysis of repairable systems with severe left censoring or truncation. Quality Engineering, 2018. 30(2): p. 329-338.
  • 7. Rausand, M. and A. Høyland, System reliability theory: models, statistical methods, and applications. Vol. 396. 2003: John Wiley & Sons.
  • 8. Cook, R.J. and J.F. Lawless, The statistical analysis of recurrent events. 2007: Springer.
  • 9. Ascher, H. and H. Feingold, Repairable systems reliability: modeling, inference, misconceptions and their causes. 1984: M. Dekker New York.
  • 10. Garmabaki, A., et al., A reliability decision framework for multiple repairable units. Reliability Engineering & System Safety, 2016. 150: p. 78-88.
  • 11. Kvaløy, J.T. and B.H. Lindqvist, TTT-based tests for trend in repairable systems data. Reliability Engineering & System Safety, 1998. 60(1): p. 13-28.
  • 12. Rigdon, S.E. and A.P. Basu, Statistical methods for the reliability of repairable systems. 2000: Wiley New York.
  • 13. Kvaløy, J.T. and B.H. Lindqvist, A class of tests for renewal process versus monotonic and nonmonotonic trend in repairable systems data, in Mathematical and Statistical Methods in Reliability. 2003, World Scientific. p. 401-414.
  • 14. Viertävä, J. and J.K. Vaurio, Testing statistical significance of trends in learning, ageing and safety indicators. Reliability Engineering & System Safety, 2009. 94(6): p. 1128-1132.
  • 15. Shen, L., B. Cassottana, and L.C. Tang, Statistical trend tests for resilience of power systems. Reliability Engineering & System Safety, 2018. 177: p. 138-147.
  • 16. Kvaløy, J.T. and B.H. Lindqvist, A class of tests for trend in time censored recurrent event data. Technometrics, 2020. 62(1): p. 101-115.
  • 17. Ascher, H.E. and C.K. Hansen, Spurious exponentiality observed when incorrectly fitting a distribution to nonstationary data. IEEE transactions on reliability, 1998. 47(4): p. 451-459.
  • 18. Ascher, H.E., A set-of-numbers is NOT a data-set. IEEE Transactions on Reliability, 1999. 48(2): p. 135-140.
  • 19. Nelson, W.B., Recurrent events data analysis for product repairs, disease recurrences, and other applications. Vol. 10. 2003: SIAM.
  • 20. Zuo, J., W.Q. Meeker, and H. Wu, A simulation study on the confidence interval procedures of some mean cumulative function estimators. Journal of Statistical Computation and Simulation, 2013. 83(10): p. 1868-1889.
  • 21. Chan, K.C.G. and M.-C. Wang, Semiparametric modeling and estimation of the terminal behavior of recurrent marker processes before failure events. Journal of the American Statistical Association, 2017. 112(517): p. 351-362.
  • 22. Nelson, W., Confidence limits for recurrence data—applied to cost or number of product repairs. Technometrics, 1995. 37(2): p. 147-157.
  • 23. Nelson, W.B., Repair Data, Sets of: How to Graph, Analyze, and Compare. Encyclopedia of Statistics in Quality and Reliability, 2008.
  • 24. Zuo, J., W.Q. Meeker, and H. Wu, Analysis of window-observation recurrence data. Technometrics, 2008. 50(2): p. 128-143.
  • 25. Jiang, R., et al., A robust mean cumulative function estimator and its application to overhaul time optimization for a fleet of heterogeneous repairable systems. Reliability Engineering & System Safety, 2023: p. 109265.
  • 26. Nelson, W.B., Repair Data, Sets of: How to Graph, Analyze, and Compare. Encyclopedia of Statistics in Quality and Reliability, 2008. 4.
  • 27. Nelson, W.B., Recurrent events data analysis for product repairs, disease recurrences, and other applications. 2003: SIAM.
  • 28. William, W. and L.A. Escobar, Statistical methods for reliability data. A. Wiley Interscience Publications, 1998.
There are 28 citations in total.

Details

Primary Language English
Subjects Industrial Engineering
Journal Section Research Articles
Authors

Selda Kapan Ulusoy 0000-0001-5604-0448

Mahmut Sami Şaşmaztürk 0000-0001-6812-5799

Early Pub Date August 27, 2023
Publication Date August 15, 2023
Submission Date October 4, 2022
Acceptance Date May 30, 2023
Published in Issue Year 2023 Volume: 7 Issue: 2

Cite

APA Kapan Ulusoy, S., & Şaşmaztürk, M. S. (2023). Non-parametric analysis of maintenance data for Attitude Indicator of a commercial aircraft fleet. International Advanced Researches and Engineering Journal, 7(2), 103-108. https://doi.org/10.35860/iarej.1184070
AMA Kapan Ulusoy S, Şaşmaztürk MS. Non-parametric analysis of maintenance data for Attitude Indicator of a commercial aircraft fleet. Int. Adv. Res. Eng. J. August 2023;7(2):103-108. doi:10.35860/iarej.1184070
Chicago Kapan Ulusoy, Selda, and Mahmut Sami Şaşmaztürk. “Non-Parametric Analysis of Maintenance Data for Attitude Indicator of a Commercial Aircraft Fleet”. International Advanced Researches and Engineering Journal 7, no. 2 (August 2023): 103-8. https://doi.org/10.35860/iarej.1184070.
EndNote Kapan Ulusoy S, Şaşmaztürk MS (August 1, 2023) Non-parametric analysis of maintenance data for Attitude Indicator of a commercial aircraft fleet. International Advanced Researches and Engineering Journal 7 2 103–108.
IEEE S. Kapan Ulusoy and M. S. Şaşmaztürk, “Non-parametric analysis of maintenance data for Attitude Indicator of a commercial aircraft fleet”, Int. Adv. Res. Eng. J., vol. 7, no. 2, pp. 103–108, 2023, doi: 10.35860/iarej.1184070.
ISNAD Kapan Ulusoy, Selda - Şaşmaztürk, Mahmut Sami. “Non-Parametric Analysis of Maintenance Data for Attitude Indicator of a Commercial Aircraft Fleet”. International Advanced Researches and Engineering Journal 7/2 (August 2023), 103-108. https://doi.org/10.35860/iarej.1184070.
JAMA Kapan Ulusoy S, Şaşmaztürk MS. Non-parametric analysis of maintenance data for Attitude Indicator of a commercial aircraft fleet. Int. Adv. Res. Eng. J. 2023;7:103–108.
MLA Kapan Ulusoy, Selda and Mahmut Sami Şaşmaztürk. “Non-Parametric Analysis of Maintenance Data for Attitude Indicator of a Commercial Aircraft Fleet”. International Advanced Researches and Engineering Journal, vol. 7, no. 2, 2023, pp. 103-8, doi:10.35860/iarej.1184070.
Vancouver Kapan Ulusoy S, Şaşmaztürk MS. Non-parametric analysis of maintenance data for Attitude Indicator of a commercial aircraft fleet. Int. Adv. Res. Eng. J. 2023;7(2):103-8.



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