Fault diagnosis of rotation vector reducer for industrial robot based on a convolutional neural network

Shuai Yang (Author), Xing Luo (Author), Chuan Li (Author)

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.

Keywords

fault diagnosis;convolutional neural networks;RV reducers;

Data

Language:	English
Year of publishing:	2021
Typology:	1.01 - Original Scientific Article
Organization:	UL FS - Faculty of Mechanical Engineering
UDC:	681.5:007.52
COBISS:	82633475
ISSN:	0039-2480
Views:	143
Downloads:	51
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary title:	Diagnosticiranje napak na reduktorjih RV za industrijske robote na osnovi konvolucijske nevronske mreže
Secondary keywords:	diagnosticiranje napak;konvolucijske nevronske mreže;reduktorji RV;
Type (COBISS):	Article
Pages:	str. 489-500
Volume:	ǂVol. ǂ67
Issue:	ǂno. ǂ10
Chronology:	Okt. 2021
DOI:	10.5545/sv-jme.2021.7284
ID:	13770059