Remaining useful life prognostics for the electro-hydraulic actuator using relevance vector machine and optimized on-line incremental learning

Jiankang Dong; Jiaqi Wang; Runxia Guo

doi:10.1051/matecconf/201927702009

All issues

Volume 277 (2019)

MATEC Web Conf., 277 (2019) 02009

References

Open Access

Issue		MATEC Web Conf. Volume 277, 2019 2018 International Joint Conference on Metallurgical and Materials Engineering (JCMME 2018)


Article Number		02009
Number of page(s)		7
Section		Data and Signal Processing
DOI		https://doi.org/10.1051/matecconf/201927702009
Published online		02 April 2019

Li H, Pan D and Chen C L P 2014 Intelligent Prognostics for Battery Health Monitoring Using the Mean Entropy and Relevance Vector Machine IEEE Trans. Systems Man & Cybernetics Systems. 44 851-862 [CrossRef] [Google Scholar]
Hu X, Jiang J, Cao D and Bo E 2016 Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling IRE Trans. Ind. Electron. 63 2645-56. [Google Scholar]
Guo R, Wang J, Zhang N and Dong J 2018 State prediction for the actuators of civil aircraft based on a fusion framework of relevance vector machine and autoregressive integrated moving average J. Syst. Control Eng. 1-13 [Google Scholar]
Liu D, Wang H, Peng Y, Xie W and Liao H 2013 Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction Energies. 6 3654-68 [CrossRef] [Google Scholar]
Liu D, Peng Y, Li J and Peng X 2013 Multiple optimized online support vector regression for adaptive time series prediction Measurement. 46 2391-404 [CrossRef] [Google Scholar]
Chen C, Kunche S, and Pecht M 2013 Incremental learning approach for improved prediction IEEE Int. Conf. Prognostics and Health Management. pp 1-7 [Google Scholar]
Lou J, Jiang Y, Shen Q, Shen Z and Wang Z 2015 Software reliability prediction via relevance vector regression Neurocomputing. 66-73 [Google Scholar]
Widodo A, Shim M C, Caesarendra W and Yang B S 2011 Intelligent prognostics for battery health monitoring based on sample entropy Expert Systems with Applications 38 11763-69 [CrossRef] [Google Scholar]

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[1] Li H, Pan D and Chen C L P 2014 Intelligent Prognostics for Battery Health Monitoring Using the Mean Entropy and Relevance Vector Machine IEEE Trans. Systems Man & Cybernetics Systems. 44 851-862 [CrossRef] [Google Scholar]

[2] Hu X, Jiang J, Cao D and Bo E 2016 Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling IRE Trans. Ind. Electron. 63 2645-56. [Google Scholar]

[3] Guo R, Wang J, Zhang N and Dong J 2018 State prediction for the actuators of civil aircraft based on a fusion framework of relevance vector machine and autoregressive integrated moving average J. Syst. Control Eng. 1-13 [Google Scholar]

[4] Liu D, Wang H, Peng Y, Xie W and Liao H 2013 Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction Energies. 6 3654-68 [CrossRef] [Google Scholar]

[5] Liu D, Peng Y, Li J and Peng X 2013 Multiple optimized online support vector regression for adaptive time series prediction Measurement. 46 2391-404 [CrossRef] [Google Scholar]

[6] Chen C, Kunche S, and Pecht M 2013 Incremental learning approach for improved prediction IEEE Int. Conf. Prognostics and Health Management. pp 1-7 [Google Scholar]

[7] Lou J, Jiang Y, Shen Q, Shen Z and Wang Z 2015 Software reliability prediction via relevance vector regression Neurocomputing. 66-73 [Google Scholar]

[8] Widodo A, Shim M C, Caesarendra W and Yang B S 2011 Intelligent prognostics for battery health monitoring based on sample entropy Expert Systems with Applications 38 11763-69 [CrossRef] [Google Scholar]