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Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life

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  1. Luping Chen & Liangjun Xu & Yilin Zhou, 2018. "Novel Approach for Lithium-Ion Battery On-Line Remaining Useful Life Prediction Based on Permutation Entropy," Energies, MDPI, vol. 11(4), pages 1-15, April.
  2. Xiaoxia Liang & Fang Duan & Ian Bennett & David Mba, 2020. "A Comprehensive Health Indicator Integrated by the Dynamic Risk Profile from Condition Monitoring Data and the Function of Financial Losses," Energies, MDPI, vol. 14(1), pages 1-25, December.
  3. 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).
  4. Ma, Rui & Mao, Di & Cao, Dongmei & Luo, Shuai & Gupta, Suraksha & Wang, Yichuan, 2024. "From vineyard to table: Uncovering wine quality for sales management through machine learning," Journal of Business Research, Elsevier, vol. 176(C).
  5. 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.
  6. Zhu, Yongmeng & Wu, Jiechang & Wu, Jun & Liu, Shuyong, 2022. "Dimensionality reduce-based for remaining useful life prediction of machining tools with multisensor fusion," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
  7. Cao, Mengda & Zhang, Tao & Liu, Yajie & Zhang, Yajun & Wang, Yu & Li, Kaiwen, 2022. "An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator," Energy, Elsevier, vol. 257(C).
  8. Liang, Tao & Wang, Fuli & Wang, Shu & Li, Kang & Mo, Xuelei & Lu, Di, 2024. "Machinery health prognostic with uncertainty for mineral processing using TSC-TimeGAN," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
  9. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  10. Shen, Sheng & Sadoughi, Mohammadkazem & Li, Meng & Wang, Zhengdao & Hu, Chao, 2020. "Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 260(C).
  11. Wen, Pengfei & Zhao, Shuai & Chen, Shaowei & Li, Yong, 2021. "A generalized remaining useful life prediction method for complex systems based on composite health indicator," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
  12. 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).
  13. Chien-Chang Hsu & Min-Sheng Chen, 2016. "Intelligent maintenance prediction system for LED wafer testing machine," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 335-342, April.
  14. Kabir, Elnaz & Guikema, Seth & Kane, Brian, 2018. "Statistical modeling of tree failures during storms," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 68-79.
  15. 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.
  16. Lei Xiao & Xiaohui Chen & Xinghui Zhang & Min Liu, 2017. "A novel approach for bearing remaining useful life estimation under neither failure nor suspension histories condition," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1893-1914, December.
  17. Cheng, Yujie & Song, Dengwei & Wang, Zhenya & Lu, Chen & Zerhouni, Noureddine, 2020. "An ensemble prognostic method for lithium-ion battery capacity estimation based on time-varying weight allocation," Applied Energy, Elsevier, vol. 266(C).
  18. Kamran Javed & Rafael Gouriveau & Xiang Li & Noureddine Zerhouni, 2018. "Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1873-1890, December.
  19. Ali Rohan, 2022. "Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)," Mathematics, MDPI, vol. 10(12), pages 1-22, June.
  20. Kim, Taejin & Lee, Gueseok & Youn, Byeng D., 2019. "PHM experimental design for effective state separation using Jensen–Shannon divergence," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
  21. 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.
  22. 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.
  23. Li, Zhixiong & Wu, Dazhong & Hu, Chao & Terpenny, Janis, 2019. "An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 110-122.
  24. Bérenger Ossété Gombé & Gwenhael Goavec Mérou & Karla Breschi & Hervé Guyennet & Jean-Michel Friedt & Violeta Felea & Kamal Medjaher, 2019. "A SAW wireless sensor network platform for industrial predictive maintenance," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1617-1628, April.
  25. Kim, Gyeongho & Kang, Yun Seok & Yang, Sang Min & Choi, Jae Gyeong & Hwang, Gahyun & Park, Hyung Wook & Lim, Sunghoon, 2025. "Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
  26. Manuel Arias Chao & Chetan Kulkarni & Kai Goebel & Olga Fink, 2021. "Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics," Data, MDPI, vol. 6(1), pages 1-14, January.
  27. Michael Sharp & Jamie Coble & Alan Nam & J Wes Hines & Belle Upadhyaya, 2015. "Lifecycle Prognostics: Transitioning between information types," Journal of Risk and Reliability, , vol. 229(4), pages 279-290, August.
  28. 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.
  29. Xiao, Dasheng & Lin, Zhifu & Yu, Aiyang & Tang, Ke & Xiao, Hong, 2024. "Data-driven method embedded physical knowledge for entire lifecycle degradation monitoring in aircraft engines," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
  30. Younghoon Lee, 2022. "Identifying Competitive Attributes Based on an Ensemble of Explainable Artificial Intelligence," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(4), pages 407-419, August.
  31. Berndt Jesenko & Christian Schlögl, 2021. "The effect of web of science subject categories on clustering: the case of data-driven methods in business and economic sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6785-6801, August.
  32. Zheng, Xiujuan & Fang, Huajing, 2015. "An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 74-82.
  33. 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.
  34. 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.
  35. Salvatore Antonio Biancardo & Francesco Abbondati & Francesca Russo & Rosa Veropalumbo & Gianluca Dell’Acqua, 2020. "A Broad-Based Decision-Making Procedure for Runway Friction Decay Analysis in Maintenance Operations," Sustainability, MDPI, vol. 12(9), pages 1-21, April.
  36. 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.
  37. 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.
  38. 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.
  39. Li, Yuan & Li, Jingwei & Wang, Huanjie & Liu, Chengbao & Tan, Jie, 2024. "Knowledge enhanced ensemble method for remaining useful life prediction under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
  40. Sara Antomarioni & Marjorie Maria Bellinello & Maurizio Bevilacqua & Filippo Emanuele Ciarapica & Renan Favarão da Silva & Gilberto Francisco Martha de Souza, 2020. "A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant," Energies, MDPI, vol. 13(23), pages 1-16, December.
  41. 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.
  42. Jain, Amit Kumar & Lad, Bhupesh Kumar, 2020. "Prognosticating RULs while exploiting the future characteristics of operating profiles," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
  43. Yu, Wennian & Kim, II Yong & Mechefske, Chris, 2020. "An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
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