Deep Transfer Network With Multi-Kernel Dynamic Distribution Adaptation for Cross-Machine Fault Diagnosis

Recently, various deep learning models, which are mainly based on data-driven algorithms, have received more and more attention in the field of intelligent fault diagnosis and prognostics. However, there are two major assumptions accepted by default in the existing studies: 1) The training (source d...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE access Ročník 9; s. 16392 - 16409
Hlavní autoři:	Lv, Mingzhu, Liu, Shixun, Su, Xiaoming, Chen, Changzheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptation Algorithms Bearings Cognitive tasks cross-machine fault diagnosis Deep transfer network Diagnostic systems Domains Fault diagnosis Feature extraction Kernel Kernel functions Machine learning multi-kernel dynamic distribution adaptation Task analysis Testing Training transfer learning Vibrations Wind turbines
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!