Diagnosticiranje napak na reduktorjih RV za industrijske robote na osnovi konvolucijske nevronske mreže
| Autor: | Chuan Li, Xing Luo, Shuai Yang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
konvolucijske nevronske mreže
Reducer Computer science business.industry Mechanical Engineering Pattern recognition RV reducers Mechanical drive Rotation matrix fault diagnosis Fault (power engineering) Convolutional neural network law.invention Industrial robot Mechanics of Materials law reduktorji RV Component (UML) convolutional neural networks Key (cryptography) Artificial intelligence udc:681.5:007.52 diagnosticiranje napak business |
| Zdroj: | Strojniški vestnik, vol. 67, no. 10, pp. 489-500, 2021. |
| ISSN: | 2536-3948 0039-2480 |
| DOI: | 10.5545/sv-jme.2021.7284 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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