2584 KB0 KB199920252021Li, ChuanLuo, XingYang, Shuaištevilka:10letnik:67str. 489-500ISSN:0039-2480DOI:10.5545/sv-jme.2021.7284COBISSID_HOST:82633475URN:URN:NBN:SI:doc-EJJQVXT9enZveza strojnih inženirjev in tehnikov Slovenije etc.Strojniški vestnikconvolutional neural networksdiagnosticiranje napakfault diagnosiskonvolucijske nevronske mrežereduktorji RVRV reducersFault diagnosis of rotation vector reducer for industrial robot based on a convolutional neural network|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 methodsTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Fakulteta za strojništvo