2021
DOI: 10.1007/s42835-021-00825-2
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Gearbox Fault Diagnosis Based on Two-Class NMF Network Under Variable Working Conditions

Abstract: The gearbox is an important part of the wind turbine, and it is also a part prone to failure. Most of the fault diagnosis methods for gearboxes lack the ability to adapt to changing operating conditions. Once the operating conditions change, it is necessary to relearn the object characteristics, otherwise it is prone to misjudgment. Therefore, this paper proposes a method based on a two-class non-negative matrix factorization (NMF) network to realize gearbox fault diagnosis under variable operating conditions … Show more

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Cited by 4 publications
(2 citation statements)
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“…At present, there is little research on the fault diagnosis of saturable reactors. However, there are abundant research achievements in fault diagnosis based on vibration signals [3][4][5][6][7]. In practical engineering, thyristor valves operate under different conditions, which can affect the vibration characteristics of saturable reactors.…”
Section: Introductionmentioning
confidence: 99%
“…At present, there is little research on the fault diagnosis of saturable reactors. However, there are abundant research achievements in fault diagnosis based on vibration signals [3][4][5][6][7]. In practical engineering, thyristor valves operate under different conditions, which can affect the vibration characteristics of saturable reactors.…”
Section: Introductionmentioning
confidence: 99%
“…A novel deep neural network which combines EMD, LSTM, and particle swarm optimization was presented in the study [50]. A method based on the usage of a two-class nonnegative matrix factorization network was proposed in [51]. There are many more applications of deep learning techniques in gearbox fault diagnosis that were published recently, which we are aware of but not mentioning here due to the scope of this article.…”
Section: Introductionmentioning
confidence: 99%