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Original Articles

Analysis of Large Heterogeneous Repairable System Reliability Data With Static System Attributes and Dynamic Sensor Measurement in Big Data Environment

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Pages 206-222 | Received 10 May 2018, Accepted 05 Apr 2019, Published online: 16 Jul 2019

References

  • Anderson, P. K., Borgan, O., Gill, R. D., and Keiding, N. (1993), Statistical Models Based on Counting Processes, New York: Springer-Verlag.
  • Apache Spark (2018), “Apache Spark Documentation,” available at https://spark.apache.org/.
  • Bacchetti, P., and Segal, M. (1995), “Survival Trees With Time-Dependent Covariates: Application to Estimating Changes in the Incubation Period of AIDS,” Lifetime Data Analysis, 1, 35–47. DOI: 10.1007/BF00985256.
  • Brentnall, A., and Cuzick, J. (2018), “Use of the Concordance Index for Predictors of Censored Survival Data,” Statistical Methods in Medical Research, 27, 2359–2373. DOI: 10.1177/0962280216680245.
  • Bou-Hamad, I. (2011), “A Review of Survival Trees,” Statistics Surveys, 5, 44–71. DOI: 10.1214/09-SS047.
  • Bou-Hamad, I., Larocque, D., Ben-Ameur, H., Masse, L., Vitaro, F., and Tremblay, R. (2009), “Discrete-Time Survival Trees,” Canadian Journal of Statistics, 37, 17–32. DOI: 10.1002/cjs.10007.
  • Breiman, L. (2001), “Random Forests,” Machine Learning, 45, 5–32. DOI: 10.1023/A:1010933404324.
  • Chipman, H. A., George, E. I., and McCulloch, R. E. (2010), “BART: Bayesian Additive Regression Trees,” The Annals of Applied Statistics, 4, 266–298. DOI: 10.1214/09-AOAS285.
  • Doganaksoy, N., Hahn, G. J., and Meeker, W. Q. (2006), “How to Analyze Reliability Data for Repairable Products,” Quality Progress, 39, 93–95.
  • Energy Information Administration (2017), “U.S. Oil and Natural Gas Wells by Production Rate,” available at https://www.eia.gov/petroleum/wells/.
  • Fan, J., Nunn, M., and Su, X. G. (2009), “Multivariate Exponential Survival Trees and Their Application to Tooth Prognosis,” Computational Statistics and Data Analysis, 53, 1110–1121. DOI: 10.1016/j.csda.2008.10.019.
  • Fan, J., Su, X. G., and Levine, R., Nunn, M., and Leblanc, M. (2006), “Trees for Censored Survival Data by Goodness of Split, With Application to Tooth Prognosis,” Journal of the American Statistical Association, 101, 959–967. DOI: 10.1198/016214506000000438.
  • Fleming, T. R., and Harrington, D. P. (1991), Counting Processes and Survival Analysis, New York: Wiley.
  • Harrell, F., Califf, R., Pryor, D., Lee, K., and Rosati, R. (2018), “Evaluating the Yield of Medical Tests,” Journal of American Medicine Association, 247, 2543–2546. DOI: 10.1001/jama.1982.03320430047030.
  • Hastie, T., Tibshirani, R., and Friedman, J. (2009), The Elements of Statistical Learning—Data Mining, Inference, and Prediction, New York: Springer.
  • Hong, Y., Zhang, M., and Meeker, W. Q. (2018), “Big Data and Reliability Applications: The Complexity Dimension,” Journal of Quality Technology, 50, 135–149. DOI: 10.1080/00224065.2018.1438007.
  • Hothorn, T., Bühlmann, P., Dudoit, S., Molinaro, A., and Van Der Laan, M. (2006), “Survival Ensembles,” Biostatistics, 7, 355–373. DOI: 10.1093/biostatistics/kxj011.
  • Hothorn, T., Lausen, B., Bener, A., and Radespiel-Troger, M. (2004), “Bagging Survival Trees,” Statistics in Medicine, 23, 77–91. DOI: 10.1002/sim.1593.
  • Huo, X. M., Kim, S. B., Tsui, K. L., and Wang S. C. (2006), “FBP: A Frontier-Based Tree-Pruning Algorithm,” INFORMS Journal on Computing, 18, 494–505. DOI: 10.1287/ijoc.1050.0133.
  • Ishwaran, H., and Kogalur, U. B. (2010), “Consistency of Random Survival Forests,” Statistics & Probability Letters, 80, 1056–1064. DOI: 10.1016/j.spl.2010.02.020.
  • Ishwaran, H., Kogalur, U. B., Blackstone, E. H., and Lauer, M. S. (2008), “Random Survival Forests,” The Annals of Applied Statistics, 2, 841–860. DOI: 10.1214/08-AOAS169.
  • Klein, J. P., and Moeschberger, M. L. (2005), Survival Analysis—Techniques for Censored and Truncated Data, New York: Springer.
  • Lewis, P., and Shedler, G. (1979), “Simulation of Nonhomogenous Poisson Processes by Thinning,” Naval Research Logistics Quarterly, 26, 403–413. DOI: 10.1002/nav.3800260304.
  • Lindqvist, B. H. (2006), “On the Statistical Modeling and Analysis of Repairable Systems,” Statistical Science, 21, 532–551. DOI: 10.1214/088342306000000448.
  • Lindqvist, B. H., Elvebakk, G., and Heggland, K. (2003), “The Trend-Renewal Process for Statistical Analysis of Repairable Systems,” Technometrics, 45, 31–44. DOI: 10.1198/004017002188618671.
  • Meeker, W. Q., and Escobar, L. A. (1998), Statistical Methods for Reliability Data, New York: Wiley.
  • Meeker, W. Q., and Hong, Y. (2014), “Reliability Meets Big Data: Opportunities and Challenges,” Quality Engineering, 26, 102–116. DOI: 10.1080/08982112.2014.846119.
  • Mittman, E. T., Lewis-Beck, C., and Meeker, W. (2018), “A Hierarchical Model for Heterogenous Reliability Field Data,” Technometrics, to appear. DOI: 10.1080/00401706.2018.1518273.
  • Nelson, W. (1995), “Confidence Limits for Recurrence Data: Applied to Cost or Number of Product Repairs,” Technometrics, 37, 147–157. DOI: 10.1080/00401706.1995.10484299.
  • Pan, R., and Rigdon, S. E. (2009), “Bayes Inference for General Repairable Systems,” Journal of Quality Technology, 41, 82–94. DOI: 10.1080/00224065.2009.11917762.
  • Pratola, M. T., and Higdon, D. (2016), “Bayesian Additive Regression Tree Calibration of Complex High-Dimensional Computer Models,” Technometrics, 58, 166–179. DOI: 10.1080/00401706.2015.1049749.
  • Pulcini, G. (2001), “A Bounded Intensity Process for the Reliability of Repairable Equipment,” Journal of Quality Technology, 33, 480–492. DOI: 10.1080/00224065.2001.11980106.
  • Rigdon, S. E., and Basu, A. P. (2000), Statistical Methods for the Reliability of Repairable Systems, New York: Wiley.
  • Stocker, R., and Pena, E. A. (2007), “A General Class of Parametric Models for Recurrent Event Data,” Technometrics, 49, 210–221. DOI: 10.1198/004017007000000056.
  • Terenin, A., Dong, S. F., and Draper, D. (2017). “GPU-Accelerated Gibbs Sampling: A Case Study of the Horseshoe Probit Model,” arXiv no. 1608.04329v4.
  • Xu, Z., Hong, Y., Meeker, W., Osborn, B. E., and Illouz, K. (2017), “A Multi-Level Trend-Renewal Process for Modeling Systems With Recurrence Data,” Technometrics, 59, 225–236. DOI: 10.1080/00401706.2016.1164758.
  • Yang, Q., Hong, Y., Chen, Y., and Shi, J. (2012), “Failure Profile Analysis of Complex Repairable Systems With Multiple Failure Modes,” IEEE Transactions on Reliability, 66, 180–191. DOI: 10.1109/TR.2011.2182225.
  • Ye, Z., Hong, Y., and Xie, Y. (2013), “How Do Heterogeneities in Operational Environments Affect Field Failures,” The Annals of Applied Statistics, 7, 2249–2271. DOI: 10.1214/13-AOAS666.
  • Ye, Z., Murthy, D. N. P., Xie, M., and Tang, L. C. (2013), “Optimal Burn-in for Repairable Products Sold With a Two-Dimensional Warranty,” IEEE Transactions on Reliability, 45, 164–176. DOI: 10.1080/0740817X.2012.677573.
  • Zuo, J., Wu, H., and Meeker, W. Q. (2012), “Asymptotic Properties of Some Estimators of the Mean Cumulative Function,” Journal of Statistical Planning and Inference, 142, 2943–2952. DOI: 10.1016/j.jspi.2012.04.010.

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