PANG, Xinyu ;CHENG, Baoan ;YANG, Zhaojian ;LI, Feng . A Fault Feature Extraction Method for a Gearbox with a Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelet Neighbouring Coefficients. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 65, n.1, p. 3-11, january 2019. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/a-fault-feature-extraction-method-for-gearbox-with-composite-gear-train-based-on-eemd-and-translation-invariant-multiwavelets-neighboring-coefficients/>. Date accessed: 23 apr. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2018.5441.
Pang, X., Cheng, B., Yang, Z., & Li, F. (2019). A Fault Feature Extraction Method for a Gearbox with a Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelet Neighbouring Coefficients. Strojniški vestnik - Journal of Mechanical Engineering, 65(1), 3-11. doi:http://dx.doi.org/10.5545/sv-jme.2018.5441
@article{sv-jmesv-jme.2018.5441,
author = {Xinyu Pang and Baoan Cheng and Zhaojian Yang and Feng Li},
title = {A Fault Feature Extraction Method for a Gearbox with a Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelet Neighbouring Coefficients},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {65},
number = {1},
year = {2019},
keywords = {composite gear train; feature extraction; EEMD; multiwavelet neighbouring coefficients},
abstract = {Although gearboxes with composite gear trains have been widely used in industrial production, it remains difficult to extract their fault signal features due to relatively complex vibration signals. This paper proposes an effective fault feature extraction method based on ensemble empirical mode decomposition (EEMD) and translation-invariant multiwavelet neighbouring coefficients, through which a clear envelope spectrum of gearbox vibration signals can be obtained. Compared with EEMD denoising or translation-invariant multiwavelet denoising using neighbouring coefficients alone, the method combining both of the denoising approaches can not only effectively suppress the signal noise but also fully retain the fault feature information. The presented method was further experimentally verified using a test rig for a gearbox with a composite gear train. Fault diagnosis was conducted with a single fault, such as snaggletooth and abrasion, as well as mixed faults at different locations. The results have shown that this method can effectively extract the fault features and improve the fault detection rate of a gearbox with a composite gear train.},
issn = {0039-2480}, pages = {3-11}, doi = {10.5545/sv-jme.2018.5441},
url = {https://www.sv-jme.eu/sl/article/a-fault-feature-extraction-method-for-gearbox-with-composite-gear-train-based-on-eemd-and-translation-invariant-multiwavelets-neighboring-coefficients/}
}
Pang, X.,Cheng, B.,Yang, Z.,Li, F. 2019 January 65. A Fault Feature Extraction Method for a Gearbox with a Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelet Neighbouring Coefficients. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 65:1
%A Pang, Xinyu %A Cheng, Baoan %A Yang, Zhaojian %A Li, Feng %D 2019 %T A Fault Feature Extraction Method for a Gearbox with a Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelet Neighbouring Coefficients %B 2019 %9 composite gear train; feature extraction; EEMD; multiwavelet neighbouring coefficients %! A Fault Feature Extraction Method for a Gearbox with a Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelet Neighbouring Coefficients %K composite gear train; feature extraction; EEMD; multiwavelet neighbouring coefficients %X Although gearboxes with composite gear trains have been widely used in industrial production, it remains difficult to extract their fault signal features due to relatively complex vibration signals. This paper proposes an effective fault feature extraction method based on ensemble empirical mode decomposition (EEMD) and translation-invariant multiwavelet neighbouring coefficients, through which a clear envelope spectrum of gearbox vibration signals can be obtained. Compared with EEMD denoising or translation-invariant multiwavelet denoising using neighbouring coefficients alone, the method combining both of the denoising approaches can not only effectively suppress the signal noise but also fully retain the fault feature information. The presented method was further experimentally verified using a test rig for a gearbox with a composite gear train. Fault diagnosis was conducted with a single fault, such as snaggletooth and abrasion, as well as mixed faults at different locations. The results have shown that this method can effectively extract the fault features and improve the fault detection rate of a gearbox with a composite gear train. %U https://www.sv-jme.eu/sl/article/a-fault-feature-extraction-method-for-gearbox-with-composite-gear-train-based-on-eemd-and-translation-invariant-multiwavelets-neighboring-coefficients/ %0 Journal Article %R 10.5545/sv-jme.2018.5441 %& 3 %P 9 %J Strojniški vestnik - Journal of Mechanical Engineering %V 65 %N 1 %@ 0039-2480 %8 2019-01-14 %7 2019-01-14
Pang, Xinyu, Baoan Cheng, Zhaojian Yang, & Feng Li. "A Fault Feature Extraction Method for a Gearbox with a Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelet Neighbouring Coefficients." Strojniški vestnik - Journal of Mechanical Engineering [Online], 65.1 (2019): 3-11. Web. 23 Apr. 2024
TY - JOUR AU - Pang, Xinyu AU - Cheng, Baoan AU - Yang, Zhaojian AU - Li, Feng PY - 2019 TI - A Fault Feature Extraction Method for a Gearbox with a Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelet Neighbouring Coefficients JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2018.5441 KW - composite gear train; feature extraction; EEMD; multiwavelet neighbouring coefficients N2 - Although gearboxes with composite gear trains have been widely used in industrial production, it remains difficult to extract their fault signal features due to relatively complex vibration signals. This paper proposes an effective fault feature extraction method based on ensemble empirical mode decomposition (EEMD) and translation-invariant multiwavelet neighbouring coefficients, through which a clear envelope spectrum of gearbox vibration signals can be obtained. Compared with EEMD denoising or translation-invariant multiwavelet denoising using neighbouring coefficients alone, the method combining both of the denoising approaches can not only effectively suppress the signal noise but also fully retain the fault feature information. The presented method was further experimentally verified using a test rig for a gearbox with a composite gear train. Fault diagnosis was conducted with a single fault, such as snaggletooth and abrasion, as well as mixed faults at different locations. The results have shown that this method can effectively extract the fault features and improve the fault detection rate of a gearbox with a composite gear train. UR - https://www.sv-jme.eu/sl/article/a-fault-feature-extraction-method-for-gearbox-with-composite-gear-train-based-on-eemd-and-translation-invariant-multiwavelets-neighboring-coefficients/
@article{{sv-jme}{sv-jme.2018.5441},
author = {Pang, X., Cheng, B., Yang, Z., Li, F.},
title = {A Fault Feature Extraction Method for a Gearbox with a Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelet Neighbouring Coefficients},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {65},
number = {1},
year = {2019},
doi = {10.5545/sv-jme.2018.5441},
url = {https://www.sv-jme.eu/sl/article/a-fault-feature-extraction-method-for-gearbox-with-composite-gear-train-based-on-eemd-and-translation-invariant-multiwavelets-neighboring-coefficients/}
}
TY - JOUR AU - Pang, Xinyu AU - Cheng, Baoan AU - Yang, Zhaojian AU - Li, Feng PY - 2019/01/14 TI - A Fault Feature Extraction Method for a Gearbox with a Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelet Neighbouring Coefficients JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 65, No 1 (2019): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2018.5441 KW - composite gear train, feature extraction, EEMD, multiwavelet neighbouring coefficients N2 - Although gearboxes with composite gear trains have been widely used in industrial production, it remains difficult to extract their fault signal features due to relatively complex vibration signals. This paper proposes an effective fault feature extraction method based on ensemble empirical mode decomposition (EEMD) and translation-invariant multiwavelet neighbouring coefficients, through which a clear envelope spectrum of gearbox vibration signals can be obtained. Compared with EEMD denoising or translation-invariant multiwavelet denoising using neighbouring coefficients alone, the method combining both of the denoising approaches can not only effectively suppress the signal noise but also fully retain the fault feature information. The presented method was further experimentally verified using a test rig for a gearbox with a composite gear train. Fault diagnosis was conducted with a single fault, such as snaggletooth and abrasion, as well as mixed faults at different locations. The results have shown that this method can effectively extract the fault features and improve the fault detection rate of a gearbox with a composite gear train. UR - https://www.sv-jme.eu/sl/article/a-fault-feature-extraction-method-for-gearbox-with-composite-gear-train-based-on-eemd-and-translation-invariant-multiwavelets-neighboring-coefficients/
Pang, Xinyu, Cheng, Baoan, Yang, Zhaojian, AND Li, Feng. "A Fault Feature Extraction Method for a Gearbox with a Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelet Neighbouring Coefficients" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 65 Number 1 (14 January 2019)
Strojniški vestnik - Journal of Mechanical Engineering 65(2019)1, 3-11
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Although gearboxes with composite gear trains have been widely used in industrial production, it remains difficult to extract their fault signal features due to relatively complex vibration signals. This paper proposes an effective fault feature extraction method based on ensemble empirical mode decomposition (EEMD) and translation-invariant multiwavelet neighbouring coefficients, through which a clear envelope spectrum of gearbox vibration signals can be obtained. Compared with EEMD denoising or translation-invariant multiwavelet denoising using neighbouring coefficients alone, the method combining both of the denoising approaches can not only effectively suppress the signal noise but also fully retain the fault feature information. The presented method was further experimentally verified using a test rig for a gearbox with a composite gear train. Fault diagnosis was conducted with a single fault, such as snaggletooth and abrasion, as well as mixed faults at different locations. The results have shown that this method can effectively extract the fault features and improve the fault detection rate of a gearbox with a composite gear train.