IDEAS home Printed from https://ideas.repec.org/r/eee/reensy/v112y2013icp165-175.html
   My bibliography  Save this item

Remaining useful life estimation based on stochastic deterioration models: A comparative study

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Zhang, Mimi & Gaudoin, Olivier & Xie, Min, 2015. "Degradation-based maintenance decision using stochastic filtering for systems under imperfect maintenance," European Journal of Operational Research, Elsevier, vol. 245(2), pages 531-541.
  2. Al-Dahidi, Sameer & Di Maio, Francesco & Baraldi, Piero & Zio, Enrico, 2016. "Remaining useful life estimation in heterogeneous fleets working under variable operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 109-124.
  3. Thanh Trung Le & Florent Chatelain & Christophe Bérenguer, 2016. "Multi-branch hidden Markov models for remaining useful life estimation of systems under multiple deterioration modes," Journal of Risk and Reliability, , vol. 230(5), pages 473-484, October.
  4. Xu, Huyang & Fard, Nasser & Fang, Yuanchen, 2020. "Time series chain graph for modeling reliability covariates in degradation process," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
  5. Fan, Yuantao & Nowaczyk, Sławomir & Rögnvaldsson, Thorsteinn, 2020. "Transfer learning for remaining useful life prediction based on consensus self-organizing models," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
  6. Hu, Changhua & Xing, Yuanxing & Du, Dangbo & Si, Xiaosheng & Zhang, Jianxun, 2023. "Remaining useful life estimation for two-phase nonlinear degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  7. Choi, Woosung & Youn, Byeng D. & Oh, Hyunseok & Kim, Nam H., 2019. "A Bayesian approach for a damage growth model using sporadically measured and heterogeneous on-site data from a steam turbine," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 137-150.
  8. Pang, Zhenan & Li, Tianmei & Pei, Hong & Si, Xiaosheng, 2023. "A condition-based prognostic approach for age- and state-dependent partially observable nonlinear degrading system," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  9. Do, M. Hung & Söffker, Dirk, 2021. "State-of-the-art in integrated prognostics and health management control for utility-scale wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
  10. Zhang, Jian-Xun & Si, Xiao-Sheng & Du, Dang-Bo & Hu, Chang-Hua & Hu, Chen, 2020. "A novel iterative approach of lifetime estimation for standby systems with deteriorating spare parts," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
  11. Pang, Zhenan & Si, Xiaosheng & Hu, Changhua & Du, Dangbo & Pei, Hong, 2021. "A Bayesian Inference for Remaining Useful Life Estimation by Fusing Accelerated Degradation Data and Condition Monitoring Data," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
  12. Ye, Zhi-Sheng & Chen, Nan & Shen, Yan, 2015. "A new class of Wiener process models for degradation analysis," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 58-67.
  13. González, Esteban Le Maitre & Desforges, Xavier & Archimède, Bernard, 2018. "Assessment method of the multicomponent systems future ability to achieve productive tasks from local prognoses," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 403-415.
  14. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
  15. Zhang, Jian-Xun & Hu, Chang-Hua & He, Xiao & Si, Xiao-Sheng & Liu, Yang & Zhou, Dong-Hua, 2017. "Lifetime prognostics for deteriorating systems with time-varying random jumps," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 338-350.
  16. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne, 2016. "Remaining useful lifetime estimation and noisy gamma deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 76-87.
  17. Estelle Deloux & Mitra Fouladirad & Christophe Bérenguer, 2016. "Health-and-usage-based maintenance policies for a partially observable deteriorating system," Journal of Risk and Reliability, , vol. 230(1), pages 120-129, February.
  18. Wang, Xiaolin & Balakrishnan, Narayanaswamy & Guo, Bo, 2014. "Residual life estimation based on a generalized Wiener degradation process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 13-23.
  19. Guishuang Tian & Shaoping Wang & Jian Shi & Yajing Qiao, 2022. "State Estimation and Remaining Useful Life Prediction of PMSTM Based on a Combination of SIR and HSMM," Sustainability, MDPI, vol. 14(24), pages 1-21, December.
  20. Zhongzhe Chen & Shuchen Cao & Zijian Mao, 2017. "Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach," Energies, MDPI, vol. 11(1), pages 1-14, December.
  21. Ahmed Ragab & Mohamed-Salah Ouali & Soumaya Yacout & Hany Osman, 2016. "Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 943-958, October.
  22. Yan, Bingxin & Ma, Xiaobing & Yang, Li & Wang, Han & Wu, Tianyi, 2020. "A novel degradation-rate-volatility related effect Wiener process model with its extension to accelerated ageing data analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
  23. Vega, Manuel A. & Hu, Zhen & Fillmore, Travis B. & Smith, Matthew D. & Todd, Michael D., 2021. "A Novel Framework for Integration of Abstracted Inspection Data and Structural Health Monitoring for Damage Prognosis of Miter Gates," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
  24. Fink, Olga & Zio, Enrico & Weidmann, Ulrich, 2014. "Predicting component reliability and level of degradation with complex-valued neural networks," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 198-206.
  25. Zhang, Fode & Ng, Hon Keung Tony & Shi, Yimin, 2020. "Mis-specification analysis of Wiener degradation models by using f-divergence with outliers," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
  26. Nguyen, Khanh T.P. & Medjaher, Kamal, 2019. "A new dynamic predictive maintenance framework using deep learning for failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 251-262.
  27. Jahani, Salman & Zhou, Shiyu & Veeramani, Dharmaraj, 2021. "Stochastic prognostics under multiple time-varying environmental factors," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
  28. Downey, Austin & Lui, Yu-Hui & Hu, Chao & Laflamme, Simon & Hu, Shan, 2019. "Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 1-12.
  29. Xiaojie Ke & Zhengguo Xu & Wenhai Wang & Youxian Sun, 2017. "Remaining useful life prediction for non-stationary degradation processes with shocks," Journal of Risk and Reliability, , vol. 231(5), pages 469-480, October.
  30. Malinowski, Simon & Chebel-Morello, Brigitte & Zerhouni, Noureddine, 2015. "Remaining useful life estimation based on discriminating shapelet extraction," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 279-288.
  31. He, Jiabei & Tian, Yi & Wu, Lifeng, 2022. "A hybrid data-driven method for rapid prediction of lithium-ion battery capacity," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
  32. Huynh, K.T. & Grall, A. & Bérenguer, C., 2017. "Assessment of diagnostic and prognostic condition indices for efficient and robust maintenance decision-making of systems subject to stress corrosion cracking," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 237-254.
  33. Zhou, Shirong & Tang, Yincai & Xu, Ancha, 2021. "A generalized Wiener process with dependent degradation rate and volatility and time-varying mean-to-variance ratio," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
  34. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.
  35. Santhosh, T.V. & Gopika, V. & Ghosh, A.K. & Fernandes, B.G., 2018. "An approach for reliability prediction of instrumentation & control cables by artificial neural networks and Weibull theory for probabilistic safety assessment of NPPs," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 31-44.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.