Wang, Xiuli; Jiang, Bin; Ding, Steven X.; Lu, Ningyun; Li, Yang:
Extended Relevance Vector Machine-Based Remaining Useful Life Prediction for DC-Link Capacitor in High-Speed Train
In: IEEE Transactions on Cybernetics, Vol. 52 (2022), No. 9, pp. 9746 - 9755
2022article/chapter in journalClosed access
Electrical Engineering and Information TechnologyFaculty of Engineering » Engineering and Information Technology » Automatic Control and Complex Systems
Related: 1 publication(s)
Title in English:
Extended Relevance Vector Machine-Based Remaining Useful Life Prediction for DC-Link Capacitor in High-Speed Train
Author:
Wang, Xiuli
ORCID
0000-0001-8237-3887ORCID iD
;Jiang, Bin
ORCID
0000-0002-9153-4360ORCID iD
Other
corresponding author
;Ding, Steven X.UDE
GND
134302427
LSF ID
2347
ORCID
0000-0002-5149-5918ORCID iD
Other
connected with university
;Lu, Ningyun
ORCID
0000-0002-9964-7677ORCID iD
;Li, Yang
ORCID
0000-0002-6094-3428ORCID iD
Year of publication:
2022
Open Access?:
Closed access
DOI
10.1109/TCYB.2020.3035796
IEEE ID
9311841
Web of Science ID
000842066600103
PubMed ID
33382664
Scopus ID
85099095554
Language of text:
English
Appears in
IEEE Transactions on Cybernetics
Title in English (abbreviated):
IEEE Trans Cybern
Place of publication:
Piscataway
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
in:
Vol. 52 (2022), No. 9, pp. 9746 - 9755
ISSN
2168-2267 Show availability online & print
ISSN
2168-2275 Show availability online & print
ZDB ID
2696559-8
Note:
ab 43.2013
Preceding:
IEEE Transactions on Systems, Man, and Cybernetics, Part B : Cybernetics (T-SMCB)
Keyword, Topic:
Capacitors ; Degradation ; extended relevance vector machine (RVM) ; first hitting time (FHT) ; Kernel ; Manifolds ; Market research ; Predictive models ; remaining useful life (RUL) prediction ; Support vector machines ; tendency degradation estimation