20.500.12556/RUL-132525 Fault diagnosis of rotation vector reducer for industrial robot based on a convolutional neural network Diagnosticiranje napak na reduktorjih RV za industrijske robote na osnovi konvolucijske nevronske mreže 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. fault diagnosis convolutional neural networks RV reducers diagnosticiranje napak konvolucijske nevronske mreže reduktorji RV true false true Angleški jezik Slovenski jezik Članek v reviji 2021-10-28 07:53:28 2021-10-28 07:53:33 2022-09-12 04:12:41 0000-00-00 00:00:00 2021 0 0 Str. 489-500 no. 10 Vol. 67 Okt. 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 681.5:007.52 0039-2480 10.5545/sv-jme.2021.7284 82633475 RAZ_Yang_Shuai_2021.pdf RAZ_Yang_Shuai_2021.pdf 1 B5EB8FC6FAC45323FB8977A9CE40114D dd75a476b6ee03cce1437b46203e701430bce53c022c4fa8805af29d33456c17 6d01318e-37b3-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=150184 https://www.sv-jme.eu/sl/article/fault-diagnosis-of-rotation-vector-reducer-for-industrial-robot-based-on-convolutional-neural-network/ 1 5b85390b-37b3-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=150183 Fakulteta za strojništvo Strojniški vestnik 0 0 0