YANG, Shuai ;LUO, Xing ;LI, Chuan . Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 67, n.10, p. 489-500, october 2021. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/fault-diagnosis-of-rotation-vector-reducer-for-industrial-robot-based-on-convolutional-neural-network/>. Date accessed: 12 nov. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2021.7284.
Yang, S., Luo, X., & Li, C. (2021). Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network. Strojniški vestnik - Journal of Mechanical Engineering, 67(10), 489-500. doi:http://dx.doi.org/10.5545/sv-jme.2021.7284
@article{sv-jmesv-jme.2021.7284,
author = {Shuai Yang and Xing Luo and Chuan Li},
title = {Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {67},
number = {10},
year = {2021},
keywords = {fault diagnosis, convolutional neural network, RV reducer},
abstract = {As a key component of a mechanical drive system, the failure of the reducer will usually cause huge economic losses and even lead to serious casualties in extreme cases. To solve this problem, a two-dimensional convolutional neural network (2D-CNN) is proposed for the fault diagnosis of the rotation vector (RV) reducer installed on the industrial robot (IR). The proposed method can automatically extract the features from the data and reduce the connections between neurons and the parameters that need to be trained with its local receptive field, weight sharing, and subsampling features. Due to the aforementioned characteristics, the efficiency of network training is significantly improved, and verified by the experimental simulations. Comparative experiments with other mainstream methods are carried out to further validate the fault classification accuracy of the proposed method. The results indicate that the proposed method out-performs all the selected methods.},
issn = {0039-2480}, pages = {489-500}, doi = {10.5545/sv-jme.2021.7284},
url = {https://www.sv-jme.eu/sl/article/fault-diagnosis-of-rotation-vector-reducer-for-industrial-robot-based-on-convolutional-neural-network/}
}
Yang, S.,Luo, X.,Li, C. 2021 October 67. Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 67:10
%A Yang, Shuai %A Luo, Xing %A Li, Chuan %D 2021 %T Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network %B 2021 %9 fault diagnosis, convolutional neural network, RV reducer %! Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network %K fault diagnosis, convolutional neural network, RV reducer %X As a key component of a mechanical drive system, the failure of the reducer will usually cause huge economic losses and even lead to serious casualties in extreme cases. To solve this problem, a two-dimensional convolutional neural network (2D-CNN) is proposed for the fault diagnosis of the rotation vector (RV) reducer installed on the industrial robot (IR). The proposed method can automatically extract the features from the data and reduce the connections between neurons and the parameters that need to be trained with its local receptive field, weight sharing, and subsampling features. Due to the aforementioned characteristics, the efficiency of network training is significantly improved, and verified by the experimental simulations. Comparative experiments with other mainstream methods are carried out to further validate the fault classification accuracy of the proposed method. The results indicate that the proposed method out-performs all the selected methods. %U https://www.sv-jme.eu/sl/article/fault-diagnosis-of-rotation-vector-reducer-for-industrial-robot-based-on-convolutional-neural-network/ %0 Journal Article %R 10.5545/sv-jme.2021.7284 %& 489 %P 12 %J Strojniški vestnik - Journal of Mechanical Engineering %V 67 %N 10 %@ 0039-2480 %8 2021-10-22 %7 2021-10-22
Yang, Shuai, Xing Luo, & Chuan Li. "Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network." Strojniški vestnik - Journal of Mechanical Engineering [Online], 67.10 (2021): 489-500. Web. 12 Nov. 2024
TY - JOUR AU - Yang, Shuai AU - Luo, Xing AU - Li, Chuan PY - 2021 TI - Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2021.7284 KW - fault diagnosis, convolutional neural network, RV reducer N2 - As a key component of a mechanical drive system, the failure of the reducer will usually cause huge economic losses and even lead to serious casualties in extreme cases. To solve this problem, a two-dimensional convolutional neural network (2D-CNN) is proposed for the fault diagnosis of the rotation vector (RV) reducer installed on the industrial robot (IR). The proposed method can automatically extract the features from the data and reduce the connections between neurons and the parameters that need to be trained with its local receptive field, weight sharing, and subsampling features. Due to the aforementioned characteristics, the efficiency of network training is significantly improved, and verified by the experimental simulations. Comparative experiments with other mainstream methods are carried out to further validate the fault classification accuracy of the proposed method. The results indicate that the proposed method out-performs all the selected methods. UR - https://www.sv-jme.eu/sl/article/fault-diagnosis-of-rotation-vector-reducer-for-industrial-robot-based-on-convolutional-neural-network/
@article{{sv-jme}{sv-jme.2021.7284},
author = {Yang, S., Luo, X., Li, C.},
title = {Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {67},
number = {10},
year = {2021},
doi = {10.5545/sv-jme.2021.7284},
url = {https://www.sv-jme.eu/sl/article/fault-diagnosis-of-rotation-vector-reducer-for-industrial-robot-based-on-convolutional-neural-network/}
}
TY - JOUR AU - Yang, Shuai AU - Luo, Xing AU - Li, Chuan PY - 2021/10/22 TI - Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 67, No 10 (2021): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2021.7284 KW - fault diagnosis, convolutional neural network, RV reducer N2 - As a key component of a mechanical drive system, the failure of the reducer will usually cause huge economic losses and even lead to serious casualties in extreme cases. To solve this problem, a two-dimensional convolutional neural network (2D-CNN) is proposed for the fault diagnosis of the rotation vector (RV) reducer installed on the industrial robot (IR). The proposed method can automatically extract the features from the data and reduce the connections between neurons and the parameters that need to be trained with its local receptive field, weight sharing, and subsampling features. Due to the aforementioned characteristics, the efficiency of network training is significantly improved, and verified by the experimental simulations. Comparative experiments with other mainstream methods are carried out to further validate the fault classification accuracy of the proposed method. The results indicate that the proposed method out-performs all the selected methods. UR - https://www.sv-jme.eu/sl/article/fault-diagnosis-of-rotation-vector-reducer-for-industrial-robot-based-on-convolutional-neural-network/
Yang, Shuai, Luo, Xing, AND Li, Chuan. "Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 67 Number 10 (22 October 2021)
Strojniški vestnik - Journal of Mechanical Engineering 67(2021)10, 489-500
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
As a key component of a mechanical drive system, the failure of the reducer will usually cause huge economic losses and even lead to serious casualties in extreme cases. To solve this problem, a two-dimensional convolutional neural network (2D-CNN) is proposed for the fault diagnosis of the rotation vector (RV) reducer installed on the industrial robot (IR). The proposed method can automatically extract the features from the data and reduce the connections between neurons and the parameters that need to be trained with its local receptive field, weight sharing, and subsampling features. Due to the aforementioned characteristics, the efficiency of network training is significantly improved, and verified by the experimental simulations. Comparative experiments with other mainstream methods are carried out to further validate the fault classification accuracy of the proposed method. The results indicate that the proposed method out-performs all the selected methods.