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1
Bearing Fault Diagnosis Based on RF-PCA-LSTM Model:
, In:
2021 2nd Information Communication Technologies Conference (ICTC)
,
Zhou, Hanting
;
Cheng, Longsheng
;
Teng, Lehua
. - p. 278-282 , 2021
Link:
https://doi.org/10.1109/ICTC51749.2021.9441578
RT T1 <cTitle>2021 2nd Information Communication Technologies Conference (ICTC)</cTitle>: T1 <Title>Bearing Fault Diagnosis Based on RF-PCA-LSTM Model</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9441578&Exemplar=1&LAN=DE A1 Zhou, Hanting A1 Cheng, Longsheng A1 Teng, Lehua A1 Sun, Huiming YR 2021 K1 Fault diagnosis K1 Dimensionality reduction K1 Vibrations K1 Radio frequency K1 Employee welfare K1 Empirical mode decomposition K1 Frequency-domain analysis K1 rolling bearing K1 feature engineering K1 fault diagnosis K1 ensemble empirical mode decomposition (EEMD) K1 random forest (RF) K1 principal component analysis (PCA) K1 long short-term memory (LSTM) neural network SP 278 OP 282 LK http://dx.doi.org/https://doi.org/10.1109/ICTC51749.2021.9441578 DO https://doi.org/10.1109/ICTC51749.2021.9441578 SF ELIB - SuUB Bremen
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