Prognostics for an actuator with the combination of support vector regression and particle filter

Runxia Guo; Jianfei Sui; Jiusheng Chen

doi:10.1051/matecconf/201927702008

All issues

Volume 277 (2019)

MATEC Web Conf., 277 (2019) 02008

References

Open Access

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


Article Number		02008
Number of page(s)		7
Section		Data and Signal Processing
DOI		https://doi.org/10.1051/matecconf/201927702008
Published online		02 April 2019

V N Vapnik 2002 The nature of statistical learning theory IEEE Trans. Neural Netw 8 1564 [Google Scholar]
Khelif, Racha, Chebel-Morello, Brigitte, Malinowski and Simon 2017 Direct remaining useful life estimation based on support vector regression IEEE Trans. Ind. Electron 64 2276-85 [Google Scholar]
N J Gordon and A F M Smith 1993 Novel approach to nonlinear/non-Gaussian Bayesian state estimation IEE Proceedings-F 140 107-113 [Google Scholar]
Kendall M 1938 A new measure of rank correlation Biometrika 30 81-89 [Google Scholar]
C Sbarufatti, M Corbetta, M Giglio and F Cadini 2017 Adaptive prognosis of lithiumion batteries based on the combination of particle filters and radial basis function neural networks J. Power Sources 344 128-140 [Google Scholar]
H Yang, P Baraldi, FD Maio and E Zio 2015 A particle filtering and kernel smoothingbased approach for new design component prognostics Reliab Eng Syst Safe 134 19-31 [Google Scholar]

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[1] V N Vapnik 2002 The nature of statistical learning theory IEEE Trans. Neural Netw 8 1564 [Google Scholar]

[2] Khelif, Racha, Chebel-Morello, Brigitte, Malinowski and Simon 2017 Direct remaining useful life estimation based on support vector regression IEEE Trans. Ind. Electron 64 2276-85 [Google Scholar]

[3] N J Gordon and A F M Smith 1993 Novel approach to nonlinear/non-Gaussian Bayesian state estimation IEE Proceedings-F 140 107-113 [Google Scholar]

[4] Kendall M 1938 A new measure of rank correlation Biometrika 30 81-89 [Google Scholar]

[5] C Sbarufatti, M Corbetta, M Giglio and F Cadini 2017 Adaptive prognosis of lithiumion batteries based on the combination of particle filters and radial basis function neural networks J. Power Sources 344 128-140 [Google Scholar]

[6] H Yang, P Baraldi, FD Maio and E Zio 2015 A particle filtering and kernel smoothingbased approach for new design component prognostics Reliab Eng Syst Safe 134 19-31 [Google Scholar]