A Transient Feature Learning-Based Intelligent Fault Diagnosis Method for Planetary Gearboxes

509 Ogledov
749 Prenosov
Izvoz citacije: ABNT
QIN, Bo ;LI, Zixian ;QIN, Yan .
A Transient Feature Learning-Based Intelligent Fault Diagnosis Method for Planetary Gearboxes. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 66, n.6, p. 385-394, june 2020. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/a-transient-feature-presentation-based-intelligent-fault-diagnosis-method-for-planetary-gearbox/>. Date accessed: 25 jan. 2021. 
doi:http://dx.doi.org/10.5545/sv-jme.2020.6546.
Qin, B., Li, Z., & Qin, Y.
(2020).
A Transient Feature Learning-Based Intelligent Fault Diagnosis Method for Planetary Gearboxes.
Strojniški vestnik - Journal of Mechanical Engineering, 66(6), 385-394.
doi:http://dx.doi.org/10.5545/sv-jme.2020.6546
@article{sv-jmesv-jme.2020.6546,
	author = {Bo  Qin and Zixian  Li and Yan  Qin},
	title = {A Transient Feature Learning-Based Intelligent Fault Diagnosis Method for Planetary Gearboxes},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {66},
	number = {6},
	year = {2020},
	keywords = {transient features; kurtosis information; extreme learning machine; variational mode decomposition; fault diagnosis for planetary gearbox},
	abstract = {Sensitive and accurate fault features from the vibration signals of planetary gearboxes are essential for fault diagnosis, in which extreme learning machine (ELM) techniques have been widely adopted. To increase the sensitivity of extracted features fed in ELM, a novel feature extraction method is put forward, which takes advantage of the transient dynamics and the reconstructed high-dimensional data from the original vibration signal. First, based on fast kurtosis analysis, the range of transient dynamics of a vibration signal is located. Next, with the extracted kurtosis information, with variational mode decomposition, a series of intrinsic mode functions are decomposed; the ones that fall into the obtained ranges are selected as transient features, corresponding to maximum kurtosis value. Fed by the transient features, a hierarchical ELM model is well-trained for fault classification. Furthermore, a denoising auto-encoder is used to optimize input weight and threshold of implicit learning node of ELM, satisfying orthogonal condition to realize the layering of its hidden layers. Finally, a numerical case and an experiment are conducted to verify the performance of the proposed method. In comparison with its counterparts, the proposed method has a better classification accuracy in the aiding of transient features.},
	issn = {0039-2480},	pages = {385-394},	doi = {10.5545/sv-jme.2020.6546},
	url = {https://www.sv-jme.eu/sl/article/a-transient-feature-presentation-based-intelligent-fault-diagnosis-method-for-planetary-gearbox/}
}
Qin, B.,Li, Z.,Qin, Y.
2020 June 66. A Transient Feature Learning-Based Intelligent Fault Diagnosis Method for Planetary Gearboxes. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 66:6
%A Qin, Bo 
%A Li, Zixian 
%A Qin, Yan 
%D 2020
%T A Transient Feature Learning-Based Intelligent Fault Diagnosis Method for Planetary Gearboxes
%B 2020
%9 transient features; kurtosis information; extreme learning machine; variational mode decomposition; fault diagnosis for planetary gearbox
%! A Transient Feature Learning-Based Intelligent Fault Diagnosis Method for Planetary Gearboxes
%K transient features; kurtosis information; extreme learning machine; variational mode decomposition; fault diagnosis for planetary gearbox
%X Sensitive and accurate fault features from the vibration signals of planetary gearboxes are essential for fault diagnosis, in which extreme learning machine (ELM) techniques have been widely adopted. To increase the sensitivity of extracted features fed in ELM, a novel feature extraction method is put forward, which takes advantage of the transient dynamics and the reconstructed high-dimensional data from the original vibration signal. First, based on fast kurtosis analysis, the range of transient dynamics of a vibration signal is located. Next, with the extracted kurtosis information, with variational mode decomposition, a series of intrinsic mode functions are decomposed; the ones that fall into the obtained ranges are selected as transient features, corresponding to maximum kurtosis value. Fed by the transient features, a hierarchical ELM model is well-trained for fault classification. Furthermore, a denoising auto-encoder is used to optimize input weight and threshold of implicit learning node of ELM, satisfying orthogonal condition to realize the layering of its hidden layers. Finally, a numerical case and an experiment are conducted to verify the performance of the proposed method. In comparison with its counterparts, the proposed method has a better classification accuracy in the aiding of transient features.
%U https://www.sv-jme.eu/sl/article/a-transient-feature-presentation-based-intelligent-fault-diagnosis-method-for-planetary-gearbox/
%0 Journal Article
%R 10.5545/sv-jme.2020.6546
%& 385
%P 10
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 66
%N 6
%@ 0039-2480
%8 2020-06-17
%7 2020-06-17
Qin, Bo, Zixian  Li, & Yan  Qin.
"A Transient Feature Learning-Based Intelligent Fault Diagnosis Method for Planetary Gearboxes." Strojniški vestnik - Journal of Mechanical Engineering [Online], 66.6 (2020): 385-394. Web.  25 Jan. 2021
TY  - JOUR
AU  - Qin, Bo 
AU  - Li, Zixian 
AU  - Qin, Yan 
PY  - 2020
TI  - A Transient Feature Learning-Based Intelligent Fault Diagnosis Method for Planetary Gearboxes
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2020.6546
KW  - transient features; kurtosis information; extreme learning machine; variational mode decomposition; fault diagnosis for planetary gearbox
N2  - Sensitive and accurate fault features from the vibration signals of planetary gearboxes are essential for fault diagnosis, in which extreme learning machine (ELM) techniques have been widely adopted. To increase the sensitivity of extracted features fed in ELM, a novel feature extraction method is put forward, which takes advantage of the transient dynamics and the reconstructed high-dimensional data from the original vibration signal. First, based on fast kurtosis analysis, the range of transient dynamics of a vibration signal is located. Next, with the extracted kurtosis information, with variational mode decomposition, a series of intrinsic mode functions are decomposed; the ones that fall into the obtained ranges are selected as transient features, corresponding to maximum kurtosis value. Fed by the transient features, a hierarchical ELM model is well-trained for fault classification. Furthermore, a denoising auto-encoder is used to optimize input weight and threshold of implicit learning node of ELM, satisfying orthogonal condition to realize the layering of its hidden layers. Finally, a numerical case and an experiment are conducted to verify the performance of the proposed method. In comparison with its counterparts, the proposed method has a better classification accuracy in the aiding of transient features.
UR  - https://www.sv-jme.eu/sl/article/a-transient-feature-presentation-based-intelligent-fault-diagnosis-method-for-planetary-gearbox/
@article{{sv-jme}{sv-jme.2020.6546},
	author = {Qin, B., Li, Z., Qin, Y.},
	title = {A Transient Feature Learning-Based Intelligent Fault Diagnosis Method for Planetary Gearboxes},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {66},
	number = {6},
	year = {2020},
	doi = {10.5545/sv-jme.2020.6546},
	url = {https://www.sv-jme.eu/sl/article/a-transient-feature-presentation-based-intelligent-fault-diagnosis-method-for-planetary-gearbox/}
}
TY  - JOUR
AU  - Qin, Bo 
AU  - Li, Zixian 
AU  - Qin, Yan 
PY  - 2020/06/17
TI  - A Transient Feature Learning-Based Intelligent Fault Diagnosis Method for Planetary Gearboxes
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 66, No 6 (2020): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2020.6546
KW  - transient features, kurtosis information, extreme learning machine, variational mode decomposition, fault diagnosis for planetary gearbox
N2  - Sensitive and accurate fault features from the vibration signals of planetary gearboxes are essential for fault diagnosis, in which extreme learning machine (ELM) techniques have been widely adopted. To increase the sensitivity of extracted features fed in ELM, a novel feature extraction method is put forward, which takes advantage of the transient dynamics and the reconstructed high-dimensional data from the original vibration signal. First, based on fast kurtosis analysis, the range of transient dynamics of a vibration signal is located. Next, with the extracted kurtosis information, with variational mode decomposition, a series of intrinsic mode functions are decomposed; the ones that fall into the obtained ranges are selected as transient features, corresponding to maximum kurtosis value. Fed by the transient features, a hierarchical ELM model is well-trained for fault classification. Furthermore, a denoising auto-encoder is used to optimize input weight and threshold of implicit learning node of ELM, satisfying orthogonal condition to realize the layering of its hidden layers. Finally, a numerical case and an experiment are conducted to verify the performance of the proposed method. In comparison with its counterparts, the proposed method has a better classification accuracy in the aiding of transient features.
UR  - https://www.sv-jme.eu/sl/article/a-transient-feature-presentation-based-intelligent-fault-diagnosis-method-for-planetary-gearbox/
Qin, Bo, Li, Zixian, AND Qin, Yan.
"A Transient Feature Learning-Based Intelligent Fault Diagnosis Method for Planetary Gearboxes" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 66 Number 6 (17 June 2020)

Avtorji

Inštitucije

  • Inner Mongolia University of Science & Technology, School of Mechanical Engineering, China 1
  • Singapore University of Technology and Design, Engineering Product Development Pillar, Singapore 2

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 66(2020)6, 385-394

https://doi.org/10.5545/sv-jme.2020.6546

Sensitive and accurate fault features from the vibration signals of planetary gearboxes are essential for fault diagnosis, in which extreme learning machine (ELM) techniques have been widely adopted. To increase the sensitivity of extracted features fed in ELM, a novel feature extraction method is put forward, which takes advantage of the transient dynamics and the reconstructed high-dimensional data from the original vibration signal. First, based on fast kurtosis analysis, the range of transient dynamics of a vibration signal is located. Next, with the extracted kurtosis information, with variational mode decomposition, a series of intrinsic mode functions are decomposed; the ones that fall into the obtained ranges are selected as transient features, corresponding to maximum kurtosis value. Fed by the transient features, a hierarchical ELM model is well-trained for fault classification. Furthermore, a denoising auto-encoder is used to optimize input weight and threshold of implicit learning node of ELM, satisfying orthogonal condition to realize the layering of its hidden layers. Finally, a numerical case and an experiment are conducted to verify the performance of the proposed method. In comparison with its counterparts, the proposed method has a better classification accuracy in the aiding of transient features.

transient features; kurtosis information; extreme learning machine; variational mode decomposition; fault diagnosis for planetary gearbox