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Robust Lifetime Estimation with Small Sample Sized High Speed Railway ETCS Component Data

Yıl 2024, Cilt: 6 Sayı: 2, 181 - 207, 31.12.2024
https://doi.org/10.51541/nicel.1571005

Öz

Railway maintenance and efficient operation has been an important issue for a safe rail traffic. When an unexpected malfunction occurs on various components on the train or on the railway system, it may result in unscheduled maintenance which may cause the rail traffic to stop. In this paper, we study the random failure model of some frequently malfunctioning high-speed railway equipment based on the statistical analysis of the real data of failure records in Turkey. Popular distribution functions and parameter estimation methods have been used considering that the data has a small sample size, and it may contain outliers. In this study, we showed that for the case of a few numbers of failure data, the L-moments method gives effective results when there exists no outlier and the robust-M method gives effective results when there exists an outlier or outliers.

Kaynakça

  • Abdul-Moniem, I. and Selim, Y. M. (2009), Tl-moments and L-moments estimation for the generalized pareto distribution, Applied Mathematical Sciences, 3(1):43-52.
  • Antoni, M. (2009), The ageing of signalling equipment and the impact on maintenance strategy. In 2009 International Conference on Computers & Industrial Engineering. IEEE.
  • Arslan, T., Bulut, Y. M. and Yavuz, A. A. (2014), Comparative study of numerical methods for determining weibull parameters for wind energy potential, Renewable and Sustainable Energy Reviews, 40, 820-825.
  • Bain, L. J. and Engelhardt, M. (1991), Statistical analysis of reliability and life-testing models: Theory and methods. Number vol. 115 in Statistics, textbooks and monographs. M. Dekker, New York, 2nd ed edition.
  • Barbosa, J. F., Carlos Silverio Freire Ju´nior, R., Correia, J. A., De Jesus, A. M. and Cal¸cada, R. (2018), Analysis of the fatigue life estimators of the materials using small samples, The Journal of Strain Analysis for Engineering Design, 53(8):699-710.
  • Bartolucci, A. A., Singh, K. P., Bartolucci, A. D. and Bae, S. (1999), Applying medical survival data to esti- mate the three-parameter weibull distribution by the method of probability-weighted moments. Mathematics and computers in simulation, 48(4-6), 385-392.
  • Bemment, S. D., Goodall, R. M., Dixon, R. and Ward, C. P. (2017), Improving the reliability and availability of railway track switching by analysing historical failure data and introducing functionally redundant sub- systems. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 232(5), 1407-1424.
  • Casella, G. and Berger, R. L. (2001), Statistical Inference, Thomson Learning.
  • Cox, D. R. and Oakes, D. (2018), Analysis of survival data. Chapman and Hall/CRC.
  • G´omez, M., Corral, E., Castejon, C. and Garc´ıa-Prada, J. (2018), Effective crack detection in railway axles using vibration signals and WPT energy. Sensors, 18(5), 1603.
  • Hall, A. R. (2005), Generalized method of moments. Advanced texts in Econometrics, Oxford University Press, Oxford; New York. OCLC.
  • Hosking, J. R. (1990), L-moments: Analysis and estimation of distributions using linear combinations of order statistics, Journal of the Royal Statistical Society: Series B (Methodological), 52(1), 105-124.
  • Hosking, J. R. (2007), Distributions with maximum entropy subject to constraints on their l-moments or expected order statistics, Journal of statistical planning and inference, 137(9), 2870-2891.
  • Huber, P. (2009), Robust statistics, Wiley, Hoboken, N.J.
  • Huber-Carol, C., Balakrishnan, N., Nikulin, M. and Mesbah, M. (2012), Goodness-of-fit tests and model validity, Springer Science & Business Media.
  • Jia, L., Wang, L.,and Qin, Y. (2021), High-speed railway transportation organization status. In High-Speed Railway Operation Under Emergent Conditions, 1-29, Springer Berlin Heidelberg.
  • Lv, S., Niu, Z., He, Z. and Qu, L. (2015), Estimation of lower percentiles under a weibull distribution, In 2015 First International Conference on Reliability Systems Engineering (ICRSE). IEEE.
  • McCool, J. I. (2012), Using the Weibull Distribution: Reliability, Modeling, and Inference, Wiley.
  • Moeini, A., Jenab, K., Mohammadi, M. and Foumani, M. (2013), Fitting the three-parameter weibull distribution with cross entropy, Applied Mathematical Modelling, 37(9), 6354-6363.
  • Mokhtarian, P., Namzi-Rad, M.-R., Ho, T. K. and Suesse, T. (2013), Bayesian nonparametric reliability analysis for a railway system at component level. In 2013 IEEE International Conference on Intelligent Rail Transportation Proceedings. IEEE.
  • Shangguan, W., Zang, Y., Wang, H., and Pecht, M. G. (2020), Board-level lifetime prediction for power board of balise transmission module in high-speed railways, IEEE Access, 8, 135011-135024.
  • Sirvanci, M. and Yang, G. (1984), Estimation of the weibull parameters under type i censoring, Journal of the American Statistical Association, 79(385), 183-187.
  • Stanley, P., Hagelin, G., Heijnen, F., Lofstedt, K., Por´e, J., Suwe, K.-H. and Zoetardt, P. (2011), ETCS for Engineers, Eurail press.
  • Tsionas, E. G. (2003), Bayesian quantile inference, Journal of statistical computation and simulation, 73(9), 659-674.
  • Wang, D., Hutson, A. D. and Miecznikowski, J. C. (2010), L-moment estimation for parametric survival models given censored data, Statistical Methodology, 7(6):655-667.
  • Wang, L., Xu, Y. and Zhang, J. (2008), Research on reliability analysis model for key components and parts of railway equipment and its application, Journal of the China Railway Society, 30(4), 93-97.
  • Yang, J.-W., Wang, J.-H., Huang, Q. and Zhou, M. (2018), Reliability assessment for the solenoid valve of a high-speed train braking system under small sample size, Chinese Journal of Mechanical Engineering, 31(1).
  • Zhang, D., Long, Z., Xue, S. and Zhang, J. (2012), Optimal design of the absolute positioning sensor for a high-speed maglev train and research on its fault diagnosis, Sensors, 12(8), 10621-10638.
  • Zheng, P., Quan, S. and Chu, W. (2021), Analysis of market competitiveness of container railway transportation, Journal of Advanced Transportation, 1-8.
  • Zhu, D. and Liu, H. (2013), Reliability evaluation of high-speed train bearing with minimum sample, Journal of Central South University (Science and Technology), 44(3), 963–969.

Az Örneklemli Yüksek Hızlı Demiryolu Etcs Bileşen Verileriyle Gürbüz Ömür Süresi Tahmini

Yıl 2024, Cilt: 6 Sayı: 2, 181 - 207, 31.12.2024
https://doi.org/10.51541/nicel.1571005

Öz

Demiryolu sistemlerinin bakımı ve işletme verimliliği, güvenli bir demiryolu trafiği için önemlidir. Tren veya demiryolu sistemindeki çeşitli bileşenlerde beklenmeyen bir arıza meydana geldiğinde, demiryolu trafiğinin tamamen durmasına yol açabilecek bir zorunlu bakım gerektirebilir. Bu makalede, Türkiye'deki arıza kayıtları verilerinin istatistiksel analizine dayalı olarak sık aralıklarla arızalanan bazı yüksek hızlı demiryolu ekipmanlarının rassal arıza modeli incelenmiştir. Verilerin küçük örneklem hacmine sahip olması ve aykırı değerler içerebilme durumları da dikkate alınarak yaygın olarak kullanılan dağılım fonksiyonları ve parametre tahmin teknikleri uygulanmıştır. Bu çalışmada, az sayıda arıza verisi durumunda, aykırı değer olmadığında L-momentler yönteminin ve aykırı değer veya aykırı değerler olduğunda ise gürbüz-M yönteminin etkili sonuçlar verdiği gösterilmiştir.

Kaynakça

  • Abdul-Moniem, I. and Selim, Y. M. (2009), Tl-moments and L-moments estimation for the generalized pareto distribution, Applied Mathematical Sciences, 3(1):43-52.
  • Antoni, M. (2009), The ageing of signalling equipment and the impact on maintenance strategy. In 2009 International Conference on Computers & Industrial Engineering. IEEE.
  • Arslan, T., Bulut, Y. M. and Yavuz, A. A. (2014), Comparative study of numerical methods for determining weibull parameters for wind energy potential, Renewable and Sustainable Energy Reviews, 40, 820-825.
  • Bain, L. J. and Engelhardt, M. (1991), Statistical analysis of reliability and life-testing models: Theory and methods. Number vol. 115 in Statistics, textbooks and monographs. M. Dekker, New York, 2nd ed edition.
  • Barbosa, J. F., Carlos Silverio Freire Ju´nior, R., Correia, J. A., De Jesus, A. M. and Cal¸cada, R. (2018), Analysis of the fatigue life estimators of the materials using small samples, The Journal of Strain Analysis for Engineering Design, 53(8):699-710.
  • Bartolucci, A. A., Singh, K. P., Bartolucci, A. D. and Bae, S. (1999), Applying medical survival data to esti- mate the three-parameter weibull distribution by the method of probability-weighted moments. Mathematics and computers in simulation, 48(4-6), 385-392.
  • Bemment, S. D., Goodall, R. M., Dixon, R. and Ward, C. P. (2017), Improving the reliability and availability of railway track switching by analysing historical failure data and introducing functionally redundant sub- systems. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 232(5), 1407-1424.
  • Casella, G. and Berger, R. L. (2001), Statistical Inference, Thomson Learning.
  • Cox, D. R. and Oakes, D. (2018), Analysis of survival data. Chapman and Hall/CRC.
  • G´omez, M., Corral, E., Castejon, C. and Garc´ıa-Prada, J. (2018), Effective crack detection in railway axles using vibration signals and WPT energy. Sensors, 18(5), 1603.
  • Hall, A. R. (2005), Generalized method of moments. Advanced texts in Econometrics, Oxford University Press, Oxford; New York. OCLC.
  • Hosking, J. R. (1990), L-moments: Analysis and estimation of distributions using linear combinations of order statistics, Journal of the Royal Statistical Society: Series B (Methodological), 52(1), 105-124.
  • Hosking, J. R. (2007), Distributions with maximum entropy subject to constraints on their l-moments or expected order statistics, Journal of statistical planning and inference, 137(9), 2870-2891.
  • Huber, P. (2009), Robust statistics, Wiley, Hoboken, N.J.
  • Huber-Carol, C., Balakrishnan, N., Nikulin, M. and Mesbah, M. (2012), Goodness-of-fit tests and model validity, Springer Science & Business Media.
  • Jia, L., Wang, L.,and Qin, Y. (2021), High-speed railway transportation organization status. In High-Speed Railway Operation Under Emergent Conditions, 1-29, Springer Berlin Heidelberg.
  • Lv, S., Niu, Z., He, Z. and Qu, L. (2015), Estimation of lower percentiles under a weibull distribution, In 2015 First International Conference on Reliability Systems Engineering (ICRSE). IEEE.
  • McCool, J. I. (2012), Using the Weibull Distribution: Reliability, Modeling, and Inference, Wiley.
  • Moeini, A., Jenab, K., Mohammadi, M. and Foumani, M. (2013), Fitting the three-parameter weibull distribution with cross entropy, Applied Mathematical Modelling, 37(9), 6354-6363.
  • Mokhtarian, P., Namzi-Rad, M.-R., Ho, T. K. and Suesse, T. (2013), Bayesian nonparametric reliability analysis for a railway system at component level. In 2013 IEEE International Conference on Intelligent Rail Transportation Proceedings. IEEE.
  • Shangguan, W., Zang, Y., Wang, H., and Pecht, M. G. (2020), Board-level lifetime prediction for power board of balise transmission module in high-speed railways, IEEE Access, 8, 135011-135024.
  • Sirvanci, M. and Yang, G. (1984), Estimation of the weibull parameters under type i censoring, Journal of the American Statistical Association, 79(385), 183-187.
  • Stanley, P., Hagelin, G., Heijnen, F., Lofstedt, K., Por´e, J., Suwe, K.-H. and Zoetardt, P. (2011), ETCS for Engineers, Eurail press.
  • Tsionas, E. G. (2003), Bayesian quantile inference, Journal of statistical computation and simulation, 73(9), 659-674.
  • Wang, D., Hutson, A. D. and Miecznikowski, J. C. (2010), L-moment estimation for parametric survival models given censored data, Statistical Methodology, 7(6):655-667.
  • Wang, L., Xu, Y. and Zhang, J. (2008), Research on reliability analysis model for key components and parts of railway equipment and its application, Journal of the China Railway Society, 30(4), 93-97.
  • Yang, J.-W., Wang, J.-H., Huang, Q. and Zhou, M. (2018), Reliability assessment for the solenoid valve of a high-speed train braking system under small sample size, Chinese Journal of Mechanical Engineering, 31(1).
  • Zhang, D., Long, Z., Xue, S. and Zhang, J. (2012), Optimal design of the absolute positioning sensor for a high-speed maglev train and research on its fault diagnosis, Sensors, 12(8), 10621-10638.
  • Zheng, P., Quan, S. and Chu, W. (2021), Analysis of market competitiveness of container railway transportation, Journal of Advanced Transportation, 1-8.
  • Zhu, D. and Liu, H. (2013), Reliability evaluation of high-speed train bearing with minimum sample, Journal of Central South University (Science and Technology), 44(3), 963–969.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Hesaplamalı İstatistik, Örnekleme Teorisi
Bölüm Makaleler
Yazarlar

Hasan Serhan Yavuz 0000-0002-4944-1013

İsmail Alayoğlu 0000-0002-7291-7868

Yayımlanma Tarihi 31 Aralık 2024
Gönderilme Tarihi 21 Ekim 2024
Kabul Tarihi 24 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 6 Sayı: 2

Kaynak Göster

APA Yavuz, H. S., & Alayoğlu, İ. (2024). Robust Lifetime Estimation with Small Sample Sized High Speed Railway ETCS Component Data. Nicel Bilimler Dergisi, 6(2), 181-207. https://doi.org/10.51541/nicel.1571005