Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Li, Y.G. & Nilkitsaranont, P., 2009. "Gas turbine performance prognostic for condition-based maintenance," Applied Energy, Elsevier, vol. 86(10), pages 2152-2161, October.
- Zio, Enrico & Peloni, Giovanni, 2011. "Particle filtering prognostic estimation of the remaining useful life of nonlinear components," Reliability Engineering and System Safety, Elsevier, vol. 96(3), pages 403-409.
- 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.
- Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
- Zio, Enrico & Di Maio, Francesco, 2010. "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 49-57.
- Tinga, Tiedo, 2010. "Application of physical failure models to enable usage and load based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1061-1075.
- Chookah, M. & Nuhi, M. & Modarres, M., 2011. "A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1601-1610.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Eleftheroglou, Nick & Galanopoulos, Georgios & Loutas, Theodoros, 2024. "Similarity learning hidden semi-Markov model for adaptive prognostics of composite structures," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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).
- GarcÃa Nieto, P.J. & GarcÃa-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.
- 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.
- Jin, Guang & Matthews, David E. & Zhou, Zhongbao, 2013. "A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 7-20.
- 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).
- 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.
- 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.
- 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.
- 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.
- Son, Junbo & Zhou, Shiyu & Sankavaram, Chaitanya & Du, Xinyu & Zhang, Yilu, 2016. "Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 38-50.
- 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.
More about this item
Keywords
prognostics; integrated vehicle health management (IVHM); remaining useful life (RUL); reliability; particle filter (PF); neural network (NN); data-driven models (DDM); adoptable data-driven;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:3:y:2018:i:4:p:49-:d:180931. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.