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A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system

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Cited by:

  1. 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.
  2. Vega, Manuel A. & Hu, Zhen & Todd, Michael D., 2020. "Optimal maintenance decisions for deteriorating quoin blocks in miter gates subject to uncertainty in the condition rating protocol," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
  3. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
  4. Mengyao Gu & Youling Chen, 2019. "Two improvements of similarity-based residual life prediction methods," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 303-315, January.
  5. Bellaera, R. & Bonifetto, R. & Di Maio, F. & Pedroni, N. & Savoldi, L. & Zanino, R. & Zio, E., 2020. "Integrated deterministic and probabilistic safety assessment of a superconducting magnet cryogenic cooling circuit for nuclear fusion applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
  6. Baraldi, Piero & Mangili, Francesca & Zio, Enrico, 2013. "Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 94-108.
  7. Zhiguo Zeng & Francesco Di Maio & Enrico Zio & Rui Kang, 2017. "A hierarchical decision-making framework for the assessment of the prediction capability of prognostic methods," Journal of Risk and Reliability, , vol. 231(1), pages 36-52, February.
  8. Faisal Khan & Omer F. Eker & Atif Khan & Wasim Orfali, 2018. "Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine," Data, MDPI, vol. 3(4), pages 1-21, November.
  9. 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).
  10. An, Dawn & Choi, Joo-Ho & Kim, Nam Ho, 2013. "Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 161-169.
  11. Xi, Zhimin & Jing, Rong & Wang, Pingfeng & Hu, Chao, 2014. "A copula-based sampling method for data-driven prognostics," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 72-82.
  12. Mengyao Gu & Youling Chen, 2018. "A multi-indicator modeling method for similarity-based residual useful life estimation with two selection processes," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 987-998, October.
  13. Paulino José García Nieto & Esperanza García-Gonzalo & Antonio Bernardo Sánchez & Marta Menéndez Fernández, 2016. "A New Predictive Model Based on the ABC Optimized Multivariate Adaptive Regression Splines Approach for Predicting the Remaining Useful Life in Aircraft Engines," Energies, MDPI, vol. 9(6), pages 1-19, May.
  14. da Costa, Paulo Roberto de Oliveira & Akçay, Alp & Zhang, Yingqian & Kaymak, Uzay, 2020. "Remaining useful lifetime prediction via deep domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
  15. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
  16. An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
  17. Abdenour Soualhi & Mourad Lamraoui & Bilal Elyousfi & Hubert Razik, 2022. "PHM SURVEY: Implementation of Prognostic Methods for Monitoring Industrial Systems," Energies, MDPI, vol. 15(19), pages 1-24, September.
  18. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
  19. Likun Ren & Weimin Lv & Shiwei Jiang, 2018. "Machine prognostics based on sparse representation model," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 277-285, February.
  20. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
  21. Michele Compare & Luca Bellani & Enrico Zio, 2017. "Availability Model of a PHM-Equipped Component," Post-Print hal-01652232, HAL.
  22. Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2019. "Portfolio optimization of safety measures for the prevention of time-dependent accident scenarios," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
  23. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
  24. Ahmad Farhat & Christophe Guyeux & Abdallah Makhoul & Ali Jaber & Rami Tawil & Abbas Hijazi, 2019. "Impacts of wireless sensor networks strategies and topologies on prognostics and health management," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2129-2155, June.
  25. Sameer Al-Dahidi & Francesco Di Maio & Piero Baraldi & Enrico Zio, 2017. "A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets," Journal of Risk and Reliability, , vol. 231(4), pages 350-363, August.
  26. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne & Levrat, Eric & Iung, Benoît, 2013. "Remaining useful life estimation based on stochastic deterioration models: A comparative study," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 165-175.
  27. Bo Wu & Bo Zhang & Wei Li & Fan Jiang, 2022. "A Novel Method for Remaining Useful Life Prediction of Bearing Based on Spectrum Image Similarity Measures," Mathematics, MDPI, vol. 10(13), pages 1-10, June.
  28. Xi, Zhimin & Zhao, Xiangxue, 2019. "An enhanced copula-based method for data-driven prognostics considering insufficient training units," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 181-194.
  29. Xiaojia Wang & Ting Huang & Keyu Zhu & Xibin Zhao, 2022. "LSTM-Based Broad Learning System for Remaining Useful Life Prediction," Mathematics, MDPI, vol. 10(12), pages 1-13, June.
  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. Yuanju Qu & Zengtao Hou, 2022. "Degradation principle of machines influenced by maintenance," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1521-1530, June.
  32. Wang, Zhaoqiang & Hu, Changhua & Wang, Wenbin & Zhou, Zhijie & Si, Xiaosheng, 2014. "A case study of remaining storage life prediction using stochastic filtering with the influence of condition monitoring," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 186-195.
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