Gear Fault Diagnosis Based on VMD Sample Entropy and Discrete Hopfield Neural Network
Joint Authors
Li, Xuejun
Ding, Jiakai
Xiao, Dongming
Huang, Liangpei
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-18
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The gear fault signal has some defects such as nonstationary nonlinearity.
In order to increase the operating life of the gear, the gear operation is monitored.
A gear fault diagnosis method based on variational mode decomposition (VMD) sample entropy and discrete Hopfield neural network (DHNN) is proposed.
Firstly, the optimal VMD decomposition number is selected by the instantaneous frequency mean value.
Then, the sample entropy value of each intrinsic mode function (IMF) is extracted to form the gear feature vectors.
The gear feature vectors are coded and used as the memory prototype and memory starting point of DHNN, respectively.
Finally, the coding vector is input into DHNN to realize fault pattern recognition.
The newly defined coding rules have a significant impact on the accuracy of gear fault diagnosis.
Driven by self-associative memory, the coding of gear fault is accurately classified by DHNN.
The superiority of the VMD-DHNN method in gear fault diagnosis is verified by comparing with an advanced signal processing algorithm.
The results show that the accuracy based on VMD sample entropy and DHNN is 91.67% of the gear fault diagnosis method.
The experimental results show that the VMD method is better than the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and empirical mode decomposition (EMD), and the effect of it in the diagnosis of gear fault diagnosis is emphasized.
American Psychological Association (APA)
Ding, Jiakai& Xiao, Dongming& Huang, Liangpei& Li, Xuejun. 2020. Gear Fault Diagnosis Based on VMD Sample Entropy and Discrete Hopfield Neural Network. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1201828
Modern Language Association (MLA)
Ding, Jiakai…[et al.]. Gear Fault Diagnosis Based on VMD Sample Entropy and Discrete Hopfield Neural Network. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1201828
American Medical Association (AMA)
Ding, Jiakai& Xiao, Dongming& Huang, Liangpei& Li, Xuejun. Gear Fault Diagnosis Based on VMD Sample Entropy and Discrete Hopfield Neural Network. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1201828
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1201828