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Research Article

A neural-network-based proportional hazard model for IoT signal fusion and failure prediction

ORCID Icon, , & ORCID Icon
Pages 377-391 | Received 22 Feb 2021, Accepted 11 Jan 2022, Published online: 25 Feb 2022

References

  • Arisido, M.W., Antolini, L., Bernasconi, D.P., Valsecchi, M.G. and Rebora, P. (2019) Joint model robustness compared with the time-varying covariate Cox model to evaluate the association between a longitudinal marker and a time-to-event endpoint. BMC Medical Research Methodology, 19(1), 1–13.
  • Breslow, N.E. (1972) Discussion of the paper by DR Cox. Journal of the Royal Statistical Society, Series B, 34, 216–217.
  • Chehade, A., Bonk, S. and Liu, K. (2017) Sensory-based failure threshold estimation for remaining useful life prediction. IEEE Transactions on Reliability, 66(3), 939–949.
  • Chen, N. and Tsui, K.L. (2013) Condition monitoring and remaining useful life prediction using degradation signals: Revisited. IIE Transactions, 45(9), 939–952.
  • Ching, T., Zhu, X. and Garmire, L.X. (2018) Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Computational Biology, 14(4), e1006076.
  • Cox, D.R. (1972) Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
  • Dupuy, J.-f. and Mesbah, M. (2002) Joint modeling of event time and nonignorable missing longitudinal data. Lifetime Data Analysis, 8(2), 99–115.
  • Efron, B. (1977) The efficiency of Cox’s likelihood function for censored data. Journal of the American Statistical Association, 72(359), 557–565.
  • Faraggi, D. and Simon, R. (1995) A neural network model for survival data. Statistics in Medicine, 14(1), 73–82.
  • Gao, Y., Wen, Y. and Wu, J. (2020) A neural network-based joint prognostic model for data fusion and remaining useful life prediction. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 117–127.
  • Gebraeel, N.Z., Lawley, M.A., Li, R. and Ryan, J.K. (2005) Residual-life distributions from component degradation signals: A Bayesian approach. IIE Transactions, 37(6), 543–557.
  • Heng, A., Tan, A.C., Mathew, J., Montgomery, N., Banjevic, D. and Jardine, A.K. (2009) Intelligent condition-based prediction of machinery reliability. Mechanical Systems and Signal Processing, 23(5), 1600–1614.
  • Hertz-Picciotto, I. and Rockhill, B. (1997) Validity and efficiency of approximation methods for tied survival times in Cox regression. Biometrics, 53(3) 1151–1156.
  • Hildebrand, F.B. (1987) Introduction to Numerical Analysis: Courier Corporation.
  • Huang, R., Xi, L., Li, X., Liu, C.R., Qiu, H. and Lee, J. (2007) Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems and Signal Processing, 21(1), 193–207.
  • Jardine, A.K., Lin, D. and Banjevic, D. (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.
  • Kalbfleisch, J.D. and Prentice, R.L. (2011) The Statistical Analysis of Failure Time Data, John Wiley & Sons, Hoboken, New Jersey.
  • Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T. and Kluger, Y. (2018) DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology, 18(1), 24–35.
  • Kvamme, H., Borgan, Ø. and Scheel, I. (2019) Time-to-event prediction with neural networks and Cox regression. Journal of Machine Learning Research, 20(129), 1–30.
  • Lawless, J.F. (2011) Statistical Models and Methods for Lifetime Data, John Wiley & Sons, Hoboken, New Jersey.
  • Liang, Z., Jun, Y., Haifeng, L. and Zhenglian, S. (2011) Reliability assessment based on multivariate degradation measures and competing failure analysis. Modern Applied Science, 5(6), 232–236.
  • Liao, H., Zhao, W. and Guo, H. (2006) Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model, in Annual Reliability and Maintainability Symposium, 2006, pp. 127–132. IEEE Press, Piscataway, NJ.
  • Liu, K., Gebraeel, N.Z., and Shi, J. (2013) A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Transactions on Automation Science and Engineering, 10(3), 652–664.
  • Liu, X., Li, J., Al-Khalifa, K.N., Hamouda, A.S., Coit, D.W. and Elsayed, E.A. (2013) Condition-based maintenance for continuously monitored degrading systems with multiple failure modes. IIE Transactions, 45(4), 422–435.
  • Lu, C.J. and Meeker, W.O. (1993) Using degradation measures to estimate a time-to-failure distribution. Technometrics, 35(2), 161–174.
  • Mahamad, A.K., Saon, S. and Hiyama, T. (2010) Predicting remaining useful life of rotating machinery based artificial neural network. Computers & Mathematics with Applications, 60(4), 1078–1087.
  • Man, J. and Zhou, Q. (2018) Prediction of hard failures with stochastic degradation signals using Wiener process and proportional hazards model. Computers & Industrial Engineering, 125, 480–489.
  • Mathioudakis, K., Stamatis, A., Tsalavoutas, A. and Aretakis, N. (2002) Computer models for education on performance monitoring and diagnostics of gas turbines. International Journal of Mechanical Engineering Education, 30(3), 204–218.
  • McPherson, J.W. (2018) Reliability Physics and Engineering: Time-to-Failure Modeling, Springer, Cham, Switzerland.
  • Meeker, W.Q. and Escobar, L.A. (2014) Statistical Methods for Reliability Data, John Wiley & Sons, Hoboken, New Jersey.
  • Pecht, M.G. (2010) A prognostics and health management roadmap for information and electronics-rich systems. IEICE ESS Fundamentals Review, 3(4), 4_25–24_32.
  • Peng, W., Li, Y.-F., Yang, Y.-J., Huang, H.-Z. and Zuo, M.J. (2014) Inverse Gaussian process models for degradation analysis: A Bayesian perspective. Reliability Engineering & System Safety, 130, 175–189.
  • Prentice, R.L. (1982) Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika, 69(2), 331–342.
  • Si, X.-S., Wang, W., Hu, C.-H. and Zhou, D.-H. (2011) Remaining useful life estimation–a review on the statistical data driven approaches. European Journal of Operational Research, 213(1), 1–14.
  • Son, J., Zhou, Q., Zhou, S., Mao, X. and Salman, M. (2013) Evaluation and comparison of mixed effects model based prognosis for hard failure. IEEE Transactions on Reliability, 62(2), 379–394.
  • Therneau, T., Crowson, C. and Atkinson, E. (2021) Using time dependent covariates and time dependent coefficients in the Cox model. Survival Vignettes. https://cran.r-project.org/web/packages/survival/vignettes/timedep.pdf
  • Tsiatis, A.A. and Davidian, M. (2001) A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error. Biometrika, 88(2), 447–458.
  • Tsiatis, A.A. and Davidian, M. (2004) Joint modeling of longitudinal and time-to-event data: An overview. Statistica Sinica, 14(3), 809–834.
  • Tsiatis, A.A., Degruttola, V. and Wulfsohn, M.S. (1995) Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS. Journal of the American Statistical Association, 90(429), 27–37.
  • Wang, P. and Coit, D.W. (2007) Reliability and degradation modeling with random or uncertain failure threshold, in 2007 Annual Reliability and Maintainability Symposium, pp. 392–397. IEEE Press, Piscataway, NJ.
  • Wen, Y., Wu, J., Das, D. and Tseng, T.-L.B. (2018) Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity. Reliability Engineering & System Safety, 176, 113–124.
  • Wen, Y., Wu, J. and Yuan, Y. (2017) Multiple-phase modeling of degradation signal for condition monitoring and remaining useful life prediction. IEEE Transactions on Reliability, 66(3), 924–938.
  • Wen, Y., Wu, J., Zhou, Q. and Tseng, T.-L. (2018) Multiple-change-point modeling and exact Bayesian inference of degradation signal for prognostic improvement. IEEE Transactions on Automation Science and Engineering, 99, 1–16.
  • Wulfsohn, M.S., and Tsiatis, A.A. (1997) A joint model for survival and longitudinal data measured with error. Biometrics, 330–339.
  • Yan, F., Lin, X., Li, R. and Huang, X. (2018) Functional principal components analysis on moving time windows of longitudinal data: Dynamic prediction of times to event. Journal of the Royal Statistical Society: Series C (Applied Statistics), 67(4), 961–978.
  • Ye, Z.-S., Xie, M., Tang, L.-C. and Chen, N. (2014) Semiparametric estimation of gamma processes for deteriorating products. Technometrics, 56(4), 504–513.
  • Yousefi, S., Amrollahi, F., Amgad, M., Dong, C., Lewis, J.E., Song, C., Gutman, D.A., Halani, S.H., Vega, J.E.V. and Brat, D.J. (2017) Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Scientific Reports, 7(1), 1–11.
  • Yu, I.-T. and Fuh, C.-D. (2010) Estimation of time to hard failure distributions using a three-stage method. IEEE Transactions on Reliability, 59(2), 405–412.
  • Yu, M., Taylor, J.M.G. and Sandler, H.M. (2008) Individual prediction in prostate cancer studies using a joint longitudinal survival–cure model. Journal of the American Statistical Association, 103(481), 178–187.
  • Yue, X. and Kontar, R.A. (2021) Joint models for event prediction from time series and survival data. Technometrics, 63(4), 477–486.
  • Zhang, J., Wang, S., Chen, L., Guo, G., Chen, R. and Vanasse, A. (2019a) Time-dependent survival neural network for remaining useful life prediction. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 441–452. Springer, Cham, Switzerland.
  • Zhang, L., Lin, J., Liu, B., Zhang, Z., Yan, X. and Wei, M. (2019b) A review on deep learning applications in prognostics and health management. IEEE Access, 7, 162415–162438.
  • Zhou, Q., Son, J., Zhou, S., Mao, X. and Salman, M. (2014) Remaining useful life prediction of individual units subject to hard failure. IIE Transactions, 46(10), 1017–1030.
  • Zio, E. and Di Maio, F. (2010) A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system. Reliability Engineering & System Safety, 95(1), 49–57.

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