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...

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Veröffentlicht in:	IEEE access Jg. 9; S. 16392 - 16409
Hauptverfasser:	Lv, Mingzhu, Liu, Shixun, Su, Xiaoming, Chen, Changzheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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