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Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab

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  1. Matteo Barbieri & Khan T. P. Nguyen & Roberto Diversi & Kamal Medjaher & Andrea Tilli, 2021. "RUL prediction for automatic machines: a mixed edge-cloud solution based on model-of-signals and particle filtering techniques," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1421-1440, June.
  2. Hu, Yang & Baraldi, Piero & Di Maio, Francesco & Zio, Enrico, 2015. "A particle filtering and kernel smoothing-based approach for new design component prognostics," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 19-31.
  3. Asadzadeh, S.M. & Azadeh, A., 2014. "An integrated systemic model for optimization of condition-based maintenance with human error," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 117-131.
  4. Wang, Yiwei & Gogu, Christian & Kim, Nam H. & Haftka, Raphael T. & Binaud, Nicolas & Bes, Christian, 2019. "Noise-dependent ranking of prognostics algorithms based on discrepancy without true damage information," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 86-100.
  5. Chen, Jian & Yuan, Shenfang & Sbarufatti, Claudio & Jin, Xin, 2021. "Dual crack growth prognosis by using a mixture proposal particle filter and on-line crack monitoring," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
  6. Long, Junqi & Chen, Chuanhai & Liu, Zhifeng & Guo, Jinyan & Chen, Weizheng, 2022. "Stochastic hybrid system approach to task-orientated remaining useful life prediction under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
  7. Zhu, Ting & Wang, Wenbo & Yu, Min, 2023. "A novel hybrid scheme for remaining useful life prognostic based on secondary decomposition, BiGRU and error correction," Energy, Elsevier, vol. 276(C).
  8. 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).
  9. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
  10. Xian Wang & Zhengxiang Song & Kun Yang & Xuyang Yin & Yingsan Geng & Jianhua Wang, 2019. "State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries," Energies, MDPI, vol. 12(1), pages 1-22, January.
  11. Wang, Hai-Kun & Li, Yan-Feng & Huang, Hong-Zhong & Jin, Tongdan, 2017. "Near-extreme system condition and near-extreme remaining useful time for a group of products," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 103-110.
  12. 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.
  13. Hachem, Hassan & Vu, Hai Canh & Fouladirad, Mitra, 2024. "Different methods for RUL prediction considering sensor degradation," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
  14. Jinjiang Wang & Robert X. Gao & Zhuang Yuan & Zhaoyan Fan & Laibin Zhang, 2019. "A joint particle filter and expectation maximization approach to machine condition prognosis," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 605-621, February.
  15. Ong, Samuel & Al-Othman, Amani & Tawalbeh, Muhammad, 2023. "Emerging technologies in prognostics for fuel cells including direct hydrocarbon fuel cells," Energy, Elsevier, vol. 277(C).
  16. Jaehyeok Doh, 2023. "Bayesian inference-based prognosis of fatigue damage for MPPO polymer using Zhurkov fatigue life model," Journal of Risk and Reliability, , vol. 237(4), pages 636-653, August.
  17. 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.
  18. Bai, Guangxing & Su, Yunsheng & Rahman, Maliha Maisha & Wang, Zequn, 2023. "Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
  19. 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.
  20. Merainani, Boualem & Laddada, Sofiane & Bechhoefer, Eric & Chikh, Mohamed Abdessamed Ait & Benazzouz, Djamel, 2022. "An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples," Renewable Energy, Elsevier, vol. 182(C), pages 1141-1151.
  21. Baptista, Marcia & Henriques, Elsa M.P. & de Medeiros, Ivo P. & Malere, Joao P. & Nascimento, Cairo L. & Prendinger, Helmut, 2019. "Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 228-239.
  22. Wang, Han & Wang, Dongdong & Liu, Haoxiang & Tang, Gang, 2022. "A predictive sliding local outlier correction method with adaptive state change rate determining for bearing remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
  23. Wei Teng & Xiaolong Zhang & Yibing Liu & Andrew Kusiak & Zhiyong Ma, 2016. "Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox," Energies, MDPI, vol. 10(1), pages 1-16, December.
  24. Pradeep Kundu & Makarand S.Kulkarni & Ashish K.Darpe, 2023. "A hybrid prognosis approach for life prediction of gears subjected to progressive pitting failure mode," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1325-1346, March.
  25. Hai-Kun Wang & Yan-Feng Li & Yu Liu & Yuan-Jian Yang & Hong-Zhong Huang, 2015. "Remaining useful life estimation under degradation and shock damage," Journal of Risk and Reliability, , vol. 229(3), pages 200-208, June.
  26. Kim, Seokgoo & Choi, Joo-Ho & Kim, Nam Ho, 2022. "Inspection schedule for prognostics with uncertainty management," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
  27. 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.
  28. Aremu, Oluseun Omotola & Hyland-Wood, David & McAree, Peter Ross, 2020. "A machine learning approach to circumventing the curse of dimensionality in discontinuous time series machine data," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
  29. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Bian, Linkan & Si, Xiaosheng, 2019. "Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 88-100.
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